- OpenXAI: Towards a Transparent Evaluation of Model Explanations
- Concpt based XAI library - CXAI
- Xplique
- DALEX
- AIX360
- ALIBI - Python XAI toolkit
- Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
- Neurocartography-Tool - Global explanation - Neuron level visualization
- TorchEsegata- Github repository
- Tutorials
- An alternative perspective on problems due to XAI *Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations
| Title | Paper Title | Source Link | Code | Tags |
|---|---|---|---|---|
| Visualization of CNN | Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps | CVPR2013 | PyTorch | Visualization gradient-based saliency maps |
| Title | Paper Title | Source Link | Code | Tags |
|---|---|---|---|---|
| CAM | Learning Deep Features for Discriminative Localization | CVPR2016 | PyTorch (Official) | class activation mapping |
| LIME | “Why Should I Trust You?”Explaining the Predictions of Any Classifier | KDD2016 | PyTorch (Official) | trust a prediction |
| Title | Paper Title | Source Link | Code | Tags |
|---|---|---|---|---|
| Grad-CAM | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization | ICCV2017, CVPR2016 (original) | PyTorch | Visualization gradient-based saliency maps |
| Network Dissection | Network Dissection: Quantifying Interpretability of Deep Visual Representations | CVPR2017 | PyTorch (Official) | Visualization |
| Title | Paper Title | Source Link | Code | Tags |
|---|---|---|---|---|
| TCAV | Interpretability Beyond Feature Attribution:Quantitative Testing with Concept Activation Vectors (TCAV) | ICML 2018 | Tensorflow 1.15.2 | interpretability method |
| Interpretable CNN | Interpretable Convolutional Neural Networks | CVPR 2018 | Tensorflow 1.x | explainability by design |
| Anchors | Anchors: High-Precision Model-Agnostic Explanations | AAAI 2018 | sklearn (Official) | model-agnostic |
| Sanity Checks | Sanity checks for saliency maps | NeurIPS 2018 | PyTorch | saliency methods vs edge detector |
| Grad Cam++ | Grad Cam++:Improved Visual Explanations forDeep Convolutional Networks | WACV 2018 | PyTorch | saliency maps |
| Interpretable Basis | Interpretable Basis Decomposition for Visual Explanation | ECCV 2018 | PyTorch | ibd |
| Title | Paper Title | Source Link | Code | Tags |
|---|---|---|---|---|
| Full-grad | Full-Gradient Representation for Neural Network Visualization | NeurIPS2019 | PyTorch (Official) Tensorflow | saliency map representation |
| This looks like that | This Looks Like That: Deep Learning for Interpretable Image Recognition | NeurIPS2019 | PyTorch (Official) | object |
| Counterfactual visual explanations | Counterfactual visual explanations | ICML2019 | interpretability |
|
| concept with contribution interpretable cnn | Explaining Neural Networks Semantically and Quantitatively | ICCV 2019 | ||
| SIS | What made you do this? Understanding black-box decisions with sufficient input subsets | AISTATS 2019 - Supplementary Material | Tensorflow 1.x | |
| Filter as concept detector | Filters in Convolutional Neural Networks as Independent Detectors of Visual Concepts | ACM |
| Title | Paper Title | Source Link | Code | Tags |
|---|---|---|---|---|
| INN | Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs | ECCV 2020 | PyTorch | explainability by design |
| X-Grad CAM | Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs | PyTorch | ||
| Revisiting BP saliency | There and Back Again: Revisiting Backpropagation Saliency Methods | CVPR 2020 | PyTorch | grad cam failure noted |
| Interacting with explanation | Making deep neural networks right for the right scientific reasons by interacting with their explanations | Nature Machine Intelligence | sklearn | |
| Class specific Filters | Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters | ECCV Supplementary Material | Code - not yet updated | ICLR rejected version with reviews |
| Interpretable Decoupling | Interpretable Neural Network Decoupling | ECCV 2020 | ||
| iCaps | iCaps: An Interpretable Classifier via Disentangled Capsule Networks | ECCV Supplementary Material | ||
| VQA | Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision | ECCV 2020 | PyTorch | |
| When explanations lie | When Explanations Lie: Why Many Modified BP Attributions Fail | ICML 2020 | PyTorch | |
| Similarity models | Towards Visually Explaining Similarity Models | Arxiv | ||
| Quantify trust | How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations | NeurIPS 2020 submission | hima_lakkaraju,sameer_singh,model-agnostic |
|
| Concepts for segmentation task | ABSTRACTING DEEP NEURAL NETWORKS INTO CONCEPT GRAPHS FOR CONCEPT LEVEL INTERPRETABILITY | Arxiv | Tensorflow 1.14 | brain tumour segmentation |
| Deep Lift based Network Pruning | Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks | Arxiv NeurIPS format | nas,deep_lift |
|
| Unifed Attribution Framework | A Unified Taylor Framework for Revisiting Attribution Methods | Arxivupdated | taylor,attribution_framework |
|
| Global Cocept Attribution | Towards Global Explanations of Convolutional Neural Networks with Concept Attribution | CVPR 2020 | ||
| relevance estimation | Determining the Relevance of Features for Deep Neural Networks | ECCV 2020 | ||
| localized concept maps | Explaining AI-based Decision Support Systems using Concept Localization Maps | Arxiv | Just repository created | |
| quantify saliency | Quantifying Explainability of Saliency Methods in Deep Neural Networks | Arxiv | PyTorch | |
| generalization of LIME - MeLIME | MeLIME: Meaningful Local Explanation for Machine Learning Models | Arxiv | Tensorflow 1.15 | |
| global counterfactual explanations | Interpretable and Interactive Summaries of Actionable Recourses | Arxiv | ||
| fine grained counterfactual heatmaps | SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition | Arxiv | PyTorch | scouter |
| quantify trust | How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks | Arxiv | ||
| Non-negative concept activation vectors | IMPROVING INTERPRETABILITY OF CNN MODELS USING NON-NEGATIVE CONCEPT ACTIVATION VECTORS | Arxiv | ||
| different layer activations | Explaining Neural Networks by Decoding Layer Activations | Arxiv | ||
| concept bottleneck networks | Concept Bottleneck Models | ICML 2020 | PyTorch | |
| attribution | Visualizing the Impact of Feature Attribution Baselines | Distill | ||
| CSI | Contextual Semantic Interpretability | Arxiv | explainable_by_design |
|
| Improve black box via explanation | Introspective Learning by Distilling Knowledge from Online Self-explanation | Arxiv | kowledge_distillation |
|
| Patch explanations | Information-Theoretic Visual Explanation for Black-Box Classifiers | Arxiv | Tensorflow 1.13.1 | patch_sampling,information_theory |
| Causality | Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect | NeurIPS 2020 | PyTorch | |
| Concept in Time series data | Conceptual Explanations of Neural Network Prediction for Time Series | IJCNN 2020 | time series, see if useful someway |
|
| Explainable by Design | Trustworthy Convolutional Neural Networks:A Gradient Penalized-based Approach | Arxiv | ||
| Colorwise Saliency | Visualizing Color-wise Saliency of Black-Box Image Classification Models | Arxiv | ||
| concept based | Concept Discovery for The Interpretation of Landscape Scenicness | Downloadable File | ||
| Integrated Score CAM | IS-CAM: Integrated Score-CAM for axiomatic-based explanations | Arxiv | ||
| Grad LAM | Grad-LAM: Visualization of Deep Neural Networks for Unsupervised Learning | EURASIP 2020 | ||
| Cites TCAV | Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction | AAAI 2020 | ||
| Attribution | Learning Propagation Rules for Attribution Map Generation | Arxiv | ||
| Zoom CAM | Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels | Arxiv | must read before modularity proposal |
|
| Masking based saliency maps investigation | INVESTIGATING AND SIMPLIFYING MASKING-BASED SALIENCY MAP METHODS FOR MODEL INTERPRETABILITY | Arxiv | PyTorch | |
| Evaluation | Evaluating Attribution Methods using White-Box LSTMs | EMNLP Workshop | PyTorch | cites TCAV, says all explanations fail their test |
| Interpretable Bayesian Neural Networks | Incorporating Interpretable Output Constraints in Bayesian Neural Networks | NeurIPS 2020 | PyTorch | |
| Survey - Counterfactual explanations | Counterfactual Explanations for Machine Learning: A Review | Arxiv | ||
| Standardised Explainability | The Need for Standardised Explainability | ICML 2020 Workshop | ||
| CME | Now You See Me (CME): Concept-based Model Extraction | CIKM 2020 workshop | sklearn | |
| Q FIT | Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning | Arxiv | ||
| Outside black box | Learning outside the Black-Box: The pursuit of interpretable models | NeurIPS 2020 | sklearn | |
| Discrete Mask | Interpreting Image Classifiers by Generating Discrete Masks | IEEE - PAMI | ||
| Contrastive explanations | Learning Global Transparent Models Consistent with Local Contrastive Explanations | NeurIPS 2020 | ||
| Empirical study of Ideal Explanations | How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods | NeurIPS 2020 | tensorflow 1.15 | Example based matching library |
| This Looks Like That + Relevance | This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition | Arxiv | PyTorch | must read before relevance |
| Concept based posthoc | ProtoViewer: Visual Interpretation and Diagnostics of Deep Neural Networks with Factorized Prototypes | Paper | refer human subject experiments |
|
| Shapley Flow | Shapley Flow: A Graph-based Approach to Interpreting Model Predictions | Arxiv | ||
| Attention Vs Saliency and Beyond | The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? | Arxiv | ||
| Unification of removal methods | Feature Removal Is A Unifying Principle For Model Explanation Methods | NeurIPS 2020 workshop | PyTorch | from the authors of SHAPExtended Arxiv version |
| Robust and Stable Black Box Explanations | Robust and Stable Black Box Explanations | ICML 2020 | hima lakkaraju |
|
| Debugging test | Debugging Tests for Model Explanations | Arxiv | ||
| AISTATS 2020 submission | Ensuring Actionable Recourse via Adversarial Training | Arxiv | hima lakkaraju |
|
| Layer wise explanation | Investigating Learning in Deep Neural Networks using Layer-Wise Weight Change | ResearchGate | ||
| cites TCAV | Debiasing Convolutional Neural Networks via Meta Orthogonalization | Arxiv | Code page not found | |
| Introducing concepts | SeXAI: Introducing Concepts into Black Boxes for Explainable Artificial Intelligence | Paper | Tensorflow 1.4 | |
| Additive explainers | Learning simplified functions to understand | Paper | ||
| BIN | Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier’s Decision | Arxiv | Tensorflow 2.2 | counterfactual explanations |
| Explantion using Generative models | Explaining image classifiers by removing input features using generative models | ACCV 2020 | Tensorflow 1.12 & Pytorch 1.1 | Nguyen's paper |
| Action Recognition Explanation | Play Fair: Frame Attributions in Video Models | ACCV 2020 | PyTorch | |
| Concepts in VQA | Interpretable Visual Reasoning via Induced Symbolic Space | Arxiv | Code not yet updated, just repository created | |
| Recourses | Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses | NeurIPS 2020 | hima lakkaraju |
|
| Feature Importance of CNN | Measuring Feature Importance of Convolutional Neural Networks | IEEE | ||
| Causal Inference | Causal inference using deep neural networks | Arxiv | Keras | |
| Match up | Match Them Up: Visually Explainable Few-shot Image Classification | Arxiv | PyTorch | |
| Right for the Right Concept | Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations | Arxiv | ||
| MALC | Transparency Promotion with Model-Agnostic Linear Competitors | ICML 2020 | ||
| Shapley Taylor Index | The Shapley Taylor Interaction Index | ICML 2020 | ||
| Concept based explanation + user feedback | Teaching the Machine to Explain Itself using Domain Knowledge | Openreview | ||
| Counterfactual produces Adversarial | Semantics and explanation: why counterfactual explanations produce adversarial examples in deep neural networks | AIJ submission | ||
| MEME | MEME: Generating RNN Model Explanations via Model Extraction | OpenReview | Keras | RNN specific LIME, see if any improvisations for MACE comes from here |
| ProtoPShare | ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery | Arxiv - Accepted at ACM SIGKDD 2021 | PyTorch | Improved ProtoPNet (This looks like that) |
| RANCC | RANCC: Rationalizing Neural Networks via Concept Clustering | ACL | Tensorflow 1.x | |
| EAN | Efficient Attention Network: Accelerate Attention by Searching Where to Plug | Arxiv | PyTorch | |
| LIME Analysis | Why model why? Assessing the strengths and limitations of LIME | Arxiv | sklearn | |
| Rethink positive aggregation | Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods | ICML 2020 workshop WHI | ||
| Pixel wise interpretation metric | A Metric to Compare Pixel-wise Interpretation Methods for Neural Networks | IEEE | ||
| Latent space debiasing | Fair Attribute Classification through Latent Space De-biasing | Arxiv | PyTorch | |
| Explanation - Teacher Student | Evaluating Explanations: How much do explanations from the teacher aid students? | Arxiv | ||
| Neural Prototype Trees | Neural Prototype Trees for Interpretable Fine-grained Image Recognition | Arxiv | PyTorch | same group of This looks like that + relevance |
| FixOut | FixOut: an ensemble approach to fairer models | Paper | ||
| Concepts on Tabular data | Learning Interpretable Concept-Based Models with Human Feedback | Arxiv | ||
| BayLIME | BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations | Arxiv | Keras | |
| PPI | Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models | Arxiv | Anonymous PyTorch code link given | |
| Generalized distillation | Understanding Interpretability by generalized distillation in Supervised Classification | AAAI 2021 submission | Code will be public upon acceptance | |
| RIG | A Singular Value Perspective on Model Robustness | Arxiv | ||
| Activation analysis | Explaining Predictions of Deep Neural Classifier via Activation Analysis | Arxiv | ||
| Evaluation metrics | Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach | Arxiv | sklearn | |
| Explanations based on train set | Explainable Artificial Intelligence: How Subsets of the Training Data Affect a Prediction | Arxiv | ||
| DAX | DAX: Deep Argumentative eXplanation for Neural Networks | Arxiv | ||
| Debiased CAM | Debiased-CAM for bias-agnostic faithful visual explanations of deep convolutional networks | Arxiv | Tensorflow 2.1.0 | lot of human subject experiments found |
| Bias via explanation | Investigating Bias in Image Classification using Model Explanations | ICML WHI 2020 | ||
| Shapley Credit Allocation | On Shapley Credit Allocation for Interpretability | Arxiv | ||
| Dependency Decomposition | Dependency Decomposition and a Reject Option for Explainable Models | Arxiv | ||
| Interpretation Network | xRAI: Explainable Representations through AI | Arxiv | ||
| Explainable by Design | Evolutionary Generative Contribution Mappings | IEEE | explainable by design |
|
| Transformer Explanation | Transformer Interpretability Beyond Attention Visualization | Arxiv CVPR format | PyTorch | |
| MANE | MANE: Model-Agnostic Non-linear Explanations for Deep Learning Model | IEEE | see how similar to MAIRE |
|
| Why and Why Not Explanations | On Relating ‘Why?’ and ‘Why Not?’ Explanations | Arxiv | sklearn | gives theoretical relationship between feature importance and counterfactual techniques |
| cites ACE | Analyzing Representations inside Convolutional Neural Networks | Arxiv | PyTorch | |
| CEN | CEN: Concept Evolution Network for Image Classification Tasks | ACM RICAI 2020 | explainable by design |
|
| Quantitative evaluation metrics | Quantitative Evaluations on Saliency Methods: An Experimental Study | Arxiv | ||
| Integrating black box and Interpretable model | IB-M: A Flexible Framework to Align an Interpretable Model and a Black-box Model | IEEE - BIBM 2020 | ||
| X-GradCAM | Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs | BMVC 2020 | ||
| RCAV | Robust Semantic Interpretability: Revisiting Concept Activation Vectors | ICML WHI 2020 | PyTorch |
| Title | Paper Title | Source Link | Code | Tags |
|---|---|---|---|---|
| Debiasing concepts | Debiasing Concept Bottleneck Models with Instrumental Variables | ICLR 2021 submissions page - Accepted as Poster | causality |
|
| Prototype Trajectory | Interpretable Sequence Classification Via Prototype Trajectory | ICLR 2021 submissions page | this looks like that styled RNN |
|
| Shapley dependence assumption | Shapley explainability on the data manifold | ICLR 2021 submissions page | ||
| High dimension Shapley | Human-interpretable model explainability on high-dimensional data | ICLR 2021 submissions page | ||
| L2x like paper | A Learning Theoretic Perspective on Local Explainability | ICLR 2021 submissions page | ||
| Evaluation | Evaluation of Similarity-based Explanations | ICLR 2021 submissions page | like adebayo paper for this looks like that styled methods |
|
| Model correction | Defuse: Debugging Classifiers Through Distilling Unrestricted Adversarial Examples | ICLR 2021 submissions page | ||
| Subspace explanation | Constraint-Driven Explanations of Black-Box ML Models | ICLR 2021 submissions page | to see how close to MUSE by Hima Lakkaraju 2019 |
|
| Catastrophic forgetting | Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting | ICLR 2021 submissions page | Code available in their Supplementary zip file | |
| Non trivial counterfactual explanations | Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations | ICLR 2021 submissions page | ||
| Explainable by Design | Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces | ICLR 2021 submissions page | ||
| Gradient attribution | Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability | ICLR 2021 submissions page | looks like extension of Sixt et al paper |
|
| Mask based Explainable by Design | Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability | ICLR 2021 submissions page | ||
| NBDT - Explainable by Design | NBDT: Neural-Backed Decision Tree | ICLR 2021 submissions page | ||
| Variational Saliency Maps | Variational saliency maps for explaining model's behavior | ICLR 2021 submissions page | ||
| Network dissection with coherency or stability metric | Importance and Coherence: Methods for Evaluating Modularity in Neural Networks | ICLR 2021 submissions page | ||
| Modularity | Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks | ICLR 2021 submissions page | Code made anonymous for review, link given in paper | |
| Explainable by design | A self-explanatory method for the black problem on discrimination part of CNN | ICLR 2021 submissions page | seems concepts of game theory applied |
|
| Attention not Explanation | Why is Attention Not So Interpretable? | ICLR 2021 submissions page | ||
| Ablation Saliency | Ablation Path Saliency | ICLR 2021 submissions page | ||
| Explainable Outlier Detection | Explainable Deep One-Class Classification | ICLR 2021 submissions page | ||
| XAI without approximation | Explainable AI Wthout Interpretable Model | Arxiv | ||
| Learning theoretic Local Interpretability | A LEARNING THEORETIC PERSPECTIVE ON LOCAL EXPLAINABILITY | Arxiv | ||
| GANMEX | GANMEX: ONE-VS-ONE ATTRIBUTIONS USING GAN-BASED MODEL EXPLAINABILITY | Arxiv | ||
| Evaluating Local Explanations | Evaluating local explanation methods on ground truth | Artificial Intelligence Journal Elsevier | sklearn | |
| Structured Attention Graphs | Structured Attention Graphs for Understanding Deep Image Classifications | AAAI 2021 | PyTorch | see how close to MACE |
| Ground truth explanations | Data Representing Ground-Truth Explanations to Evaluate XAI Methods | AAAI 2021 | sklearn | trained models available in their github repository |
| AGF | Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization | AAAI 2021 | PyTorch | |
| RSP | Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations | AAAI 2021 | ||
| HyDRA | HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks | AAAI 2021 | PyTorch | |
| SWAG | SWAG: Superpixels Weighted by Average Gradients for Explanations of CNNs | WACV 2021 | ||
| FastIF | FASTIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging | Arxiv | PyTorch | |
| EVET | EVET: Enhancing Visual Explanations of Deep Neural Networks Using Image Transformations | WACV 2021 | ||
| Local Attribution Baselines | On Baselines for Local Feature Attributions | AAAI 2021 | PyTorch | |
| Differentiated Explanations | Differentiated Explanation of Deep Neural Networks with Skewed Distributions | IEEE - TPAMI journal | PyTorch | |
| Human game based survey | Explainable AI and Adoption of Algorithmic Advisors: an Experimental Study | Arxiv | ||
| Explainable by design | Learning Semantically Meaningful Features for Interpretable Classifications | Arxiv | ||
| Expred | Explain and Predict, and then Predict again | ACM WSDM 2021 | PyTorch | |
| Progressive Interpretation | An Information-theoretic Progressive Framework for Interpretation | Arxiv | PyTorch | |
| UCAM | Uncertainty Class Activation Map (U-CAM) using Gradient Certainty method | IEEE - TIP | Project Page | PyTorch |
| progressive GAN explainability- smiling dataset- ICLR 2020 group | Explaining the Black-box Smoothly - A Counterfactual Approach | Arxiv | ||
| Head pasted in another image - experimented | WHAT DO DEEP NETS LEARN? CLASS-WISE PATTERNS REVEALED IN THE INPUT SPACE | Arxiv | ||
| Model correction | ExplOrs Explanation Oracles and the architecture of explainability | Paper | ||
| Explanations - Knowledge Representation | A Basic Framework for Explanations in Argumentation | IEEE | ||
| Eigen CAM | Eigen-CAM: Visual Explanations for Deep Convolutional Neural Networks | Springer | ||
| Evaluation of Posthoc | How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations | ACM | ||
| GLocalX | GLocalX - From Local to Global Explanations of Black Box AI Models | Arxiv | ||
| Consistent Interpretations | Explainable Models with Consistent Interpretations | AAAI 2021 | ||
| SIDU | Introducing and assessing the explainable AI (XAI) method: SIDU | Arxiv | ||
| cites This looks like that | Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies | AIJ | ||
| i-Algebra | i-Algebra: Towards Interactive Interpretability of Deep Neural Networks | AAAI 2021 | ||
| Shape texture bias | SHAPE OR TEXTURE: UNDERSTANDING DISCRIMINATIVE FEATURES IN CNNS | ICLR 2021 | ||
| Class agnostic features | THE MIND’S EYE: VISUALIZING CLASS-AGNOSTIC FEATURES OF CNNS | Arxiv | ||
| IBEX | A Multi-layered Approach for Tailored Black-box Explanations | Paper | Code | |
| Relevant explanations | Learning Relevant Explanations | Paper | ||
| Guided Zoom | Guided Zoom: Zooming into Network Evidence to Refine Fine-grained Model Decisions | IEEE | ||
| XAI survey | A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks | Arxiv | ||
| Pattern theory | Convolutional Neural Network Interpretability with General Pattern Theory | Arxiv | PyTorch | |
| Gaussian Process based explanations | Bandits for Learning to Explain from Explanations | AAAI 2021 | sklearn | |
| LIFT CAM | LIFT-CAM: Towards Better Explanations for Class Activation Mapping | Arxiv | ||
| ObAIEx | Right for the Right Reasons: Making Image Classification Intuitively Explainable | Paper | tensorflow | |
| VAE based explainer | Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions | IEEE | ||
| Segmentation based explanation | Deep Co-Attention Network for Multi-View Subspace Learning | Arxiv | PyTorch | |
| Integrated CAM | INTEGRATED GRAD-CAM: SENSITIVITY-AWARE VISUAL EXPLANATION OF DEEP CONVOLUTIONAL NETWORKS VIA INTEGRATED GRADIENT-BASED SCORING | ICASSP 2021 | PyTorch | |
| Human study | VitrAI - Applying Explainable AI in the Real World | Arxiv | ||
| Attribution Mask | Attribution Mask: Filtering Out Irrelevant Features By Recursively Focusing Attention on Inputs of DNNs | Arxiv | PyTorch | |
| LIME faithfulness | What does LIME really see in images? | Arxiv | Tensorflow 1.x | |
| Assess model reliability | Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs | Arxiv | ||
| Perturbation + Gradient unification | Towards the Unification and Robustness of Perturbation and Gradient Based Explanations | Arxiv | hima lakkaraju |
|
| Gradients faithful? | Do Input Gradients Highlight Discriminative Features? | Arxiv | PyTorch | |
| Untrustworthy predictions | Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis | Arxiv | ||
| Explaining misclassification | Explaining Inaccurate Predictions of Models through k-Nearest Neighbors | Paper | cites Oscar Li AAAI 2018 prototypes paper | |
| Explanations inside predictions | Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations | AISTATS 2021 | ||
| Layerwise interpretation | LAYER-WISE INTERPRETATION OF DEEP NEURAL NETWORKS USING IDENTITY INITIALIZATION | Arxiv | ||
| Visualizing Rule Sets | Visualizing Rule Sets: Exploration and Validation of a Design Space | Arxiv | PyTorch | |
| Human experiments | Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making | IUI 2021 | ||
| Attention fine-grained classification | Interpretable Attention Guided Network for Fine-grained Visual Classification | Arxiv | ||
| Concept construction | Explaining Classifiers by Constructing Familiar Concepts | Paper | PyTorch | |
| EbD | Human-Understandable Decision Making for Visual Recognition | Arxiv | ||
| Bridging XAI algorithm , Human needs | Towards Connecting Use Cases and Methods in Interpretable Machine Learning | Arxiv | ||
| Generative trustworthy classifiers | Generative Classifiers as a Basis for Trustworthy Image Classification | Paper | Github | |
| Counterfactual explanations | Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties | AISTATS 2021 | PyTorch | |
| Role categorization of CNN units | Quantitative Effectiveness Assessment and Role Categorization of Individual Units in Convolutional Neural Networks | ICML 2021 | ||
| Non-trivial counterfactual explanations | Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations | Arxiv | ||
| NP-ProtoPNet | These do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition | IEEE | ||
| Correcting neural networks based on explanations | Refining Neural Networks with Compositional Explanations | Arxiv | Code link given in paper, but page not found | |
| Contrastive reasoning | Contrastive Reasoning in Neural Networks | Arxiv | ||
| Concept based | Intersection Regularization for Extracting Semantic Attributes | Arxiv | ||
| Boundary explanations | Boundary Attributions Provide Normal (Vector) Explanations | Arxiv | PyTorch | |
| Generative Counterfactuals | ECINN: Efficient Counterfactuals from Invertible Neural Networks | Arxiv | ||
| ICE | Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors | AAAI 2021 | ||
| Group CAM | Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks | Arxiv | PyTorch | |
| HMM interpretability | Towards interpretability of Mixtures of Hidden Markov Models | AAAI 2021 | sklearn | |
| Empirical Explainers | Efficient Explanations from Empirical Explainers | Arxiv | PyTorch | |
| FixNorm | FIXNORM: DISSECTING WEIGHT DECAY FOR TRAINING DEEP NEURAL NETWORKS | Arxiv | ||
| CoDA-Net | Convolutional Dynamic Alignment Networks for Interpretable Classifications | CVPR 2021 | Code link given in paper. Repository not yet created | |
| Like Dr. Chandru sir's (IITPKD) XAI work | Neural Response Interpretation through the Lens of Critical Pathways | Arxiv | PyTorch- Pathway GradPyTorch - ROAR | |
| Inaugment | InAugment: Improving Classifiers via Internal Augmentation | Arxiv | Code yet to be updated | |
| Gradual Grad CAM | Enhancing Deep Neural Network Saliency Visualizations with Gradual Extrapolation | Arxiv | PyTorch | |
| A-FMI | A-FMI: LEARNING ATTRIBUTIONS FROM DEEP NETWORKS VIA FEATURE MAP IMPORTANCE | Arxiv | ||
| Trust - Regression | To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions | AAAI 2021 | sklearn | |
| Concept based explanations - study | IS DISENTANGLEMENT ALL YOU NEED? COMPARING CONCEPT-BASED & DISENTANGLEMENT APPROACHES | ICLR 2021 workshop | tensorflow 2.3 | |
| Faithful attribution | Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution | Arxiv | ||
| Counterfactual explanation | Counterfactual attribute-based visual explanations for classification | Springer | ||
| User based explanations | That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems | AIJ | ||
| Human understandable concept based explanations | Towards Human-Understandable Visual Explanations: Imperceptible High-frequency Cues Can Better Be Removed | Arxiv | ||
| Improved attribution | Improving Attribution Methods by Learning Submodular Functions | Arxiv | ||
| SHAP tractability | On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results | Arxiv | ||
| SHAP explanation network | SHAPLEY EXPLANATION NETWORKS | ICLR 2021 | PyTorch | |
| Concept based dataset shift explanation | FAILING CONCEPTUALLY: CONCEPT-BASED EXPLANATIONS OF DATASET SHIFT | ICLR 2021 workshop | tensorflow 2 | |
| EbD | Towards Human-Understandable Visual Explanations: Imperceptible High-frequency Cues Can Better Be Removed | Arxiv | ||
| Evaluating CAM | Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis | Arxiv | ||
| EFC-CAM | Exclusive Feature Constrained Class Activation Mapping for Better Visual Explanation | IEEE | ||
| Causal Interpretation | Instance-wise Causal Feature Selection for Model Interpretation | Arxiv | PyTorch | |
| Fairness in Learning | Learning to Learn to be Right for the Right Reasons | Arxiv | ||
| Feature attribution correctness | Do Feature Attribution Methods Correctly Attribute Features? | Arxiv | Code not yet updated | |
| NICE | NICE: AN ALGORITHM FOR NEAREST INSTANCE COUNTERFACTUAL EXPLANATIONS | Arxiv | Own Python Package | |
| SCG | A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts | Arxiv | ||
| Visual Concepts | A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts | Arxiv | ||
| This looks like that - drawback | This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks | Arxiv | PyTorch | |
| Exemplar based classification | Visualizing Association in Exemplar-Based Classification | ICASSP 2021 | ||
| Correcting classification | CORRECTING CLASSIFICATION: A BAYESIAN FRAMEWORK USING EXPLANATION FEEDBACK TO IMPROVE CLASSIFICATION ABILITIES | Arxiv | ||
| Concept Bottleneck Networks | DO CONCEPT BOTTLENECK MODELS LEARN AS INTENDED? | ICLR workshop 2021 | ||
| Sanity for saliency | Sanity Simulations for Saliency Methods | Arxiv | ||
| Concept based explanations | Cause and Effect: Concept-based Explanation of Neural Networks | Arxiv | ||
| CLIMEP | How to Explain Neural Networks: A perspective of data space division | Arxiv | ||
| Sufficient explanations | Probabilistic Sufficient Explanations | Arxiv | Empty Repository | |
| SHAP baseline | Learning Baseline Values for Shapley Values | Arxiv | ||
| Explainable by Design | EXoN: EXplainable encoder Network | Arxiv | tensorflow 2.4.0 | explainable VAE |
| Concept based explanations | Aligning Artificial Neural Networks and Ontologies towards Explainable AI | AAAI 2021 | ||
| XAI via Bayesian teaching | ABSTRACTION, VALIDATION, AND GENERALIZATION FOR EXPLAINABLE ARTIFICIAL INTELLIGENCE | Arxiv | ||
| Explanation blind spots | DO NOT EXPLAIN WITHOUT CONTEXT: ADDRESSING THE BLIND SPOT OF MODEL EXPLANATIONS | Arxiv | ||
| BLA | Bounded logit attention: Learning to explain image classifiers | Arxiv | tensorflow | L2X++ |
| Interpretability - mathematical model | The Definitions of Interpretability and Learning of Interpretable Models | Arxiv | ||
| Similar to our ICML workshop 2021 work | The effectiveness of feature attribution methods and its correlation with automatic evaluation scores | Arxiv | ||
| EDDA | EDDA: Explanation-driven Data Augmentation to Improve Model and Explanation Alignment | Arxiv | ||
| Relevant set explanations | Efficient Explanations With Relevant Sets | Arxiv | ||
| Model transfer | Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning | Arxiv | ||
| Model correction | Finding and Fixing Spurious Patterns with Explanations | Arxiv | ||
| Neuron graph communities | On the Evolution of Neuron Communities in a Deep Learning Architecture | Arxiv | ||
| Mid level features explanations | A general approach for Explanations in terms of Middle Level Features | Arxiv | see how different from MUSE by Hima Lakkaraju group | |
| Concept based knowledge distillation | Towards Black-Box Explainability with Gaussian Discriminant Knowledge Distillation | CVPR 2021 workshop | compare and contrast with network dissection | |
| CNN high frequency bias | Dissecting the High-Frequency Bias in Convolutional Neural Networks | CVPR 2021 workshop | Tensorflow | |
| Explainable by design | Entropy-based Logic Explanations of Neural Networks | Arxiv | PyTorch | concept based |
| CALM | Keep CALM and Improve Visual Feature Attribution | Arxiv | PyTorch | |
| Relevance CAM | Relevance-CAM: Your Model Already Knows Where to Look | CVPR 2021 | PyTorch | |
| S-LIME | S-LIME: Stabilized-LIME for Model Explanation | Arxiv | sklearn | |
| Local + Global | Best of both worlds: local and global explanations with human-understandable concepts | Arxiv | Been Kim's group | |
| Guided integrated gradients | Guided Integrated Gradients: an Adaptive Path Method for Removing Noise | CVPR 2021 | ||
| Concept based | Meaningfully Explaining a Model’s Mistakes | Arxiv | ||
| Explainable by design | It’s FLAN time! Summing feature-wise latent representations for interpretability | Arxiv | ||
| SimAM | SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks | ICML 2021 | PyTorch | |
| DANCE | DANCE: Enhancing saliency maps using decoys | ICML 2021 | Tensorflow 1.x | |
| EbD Concept formation | Explore Visual Concept Formation for Image Classification | ICML 2021 | PyTorch | |
| Explainable by design | Interpretable Compositional Convolutional Neural Networks | Arxiv | ||
| Attribution aggregation | Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation | AAAI 2021 - pdf | ||
| Perturbation based activation | A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation | AAAI 2021 | ||
| Global explanations | Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition | Arxiv | Github package | |
| L2E | Learning to Explain: Generating Stable Explanations Fast | ACL 2021 | PyTorch | NLE |
| Joint Shapley | Joint Shapley values: a measure of joint feature importance | Arxiv | ||
| Explainable by design | Align Yourself: Self-supervised Pre-training for Fine-grained Recognition via Saliency Alignment | Arxiv | ||
| Explainable by design | SONG: SELF-ORGANIZING NEURAL GRAPHS | Arxiv | ||
| Explainable by design | Designing Shapelets for Interpretable Data-Agnostic Classification | AIES 2021 | sklearn | Interpretable block of time series extended to other data modalitites like image, text, tabular |
| Global explanations + Model correction | Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability | Arxiv | PyTorch | |
| HIL- Model correction | Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models | Arxiv | ||
| Activation based Cause Analysis | Activation-Based Cause Analysis Method for Neural Networks | IEEE Access 2021 | ||
| Local explanations | Leveraging Latent Features for Local Explanations | ACM SIGKDD 2021 | Amit Dhurandhar group | |
| Fairness | Adequate and fair explanations | Arxiv - Accepted in CD-MAKE 2021 | ||
| Global explanations | Finding Representative Interpretations on Convolutional Neural Networks | ICCV 2021 | ||
| Groupwise explanations | Learning Groupwise Explanations for Black-Box Models | IJCAI 2021 | PyTorch | |
| Mathematical | On Smoother Attributions using Neural Stochastic Differential Equations | IJCAI 2021 | ||
| AGI | Explaining Deep Neural Network Models with Adversarial Gradient Integration | IJCAI 2021 | PyTorch | |
| Accountable attribution | Longitudinal Distance: Towards Accountable Instance Attribution | Arxiv | Tensorflow Keras | |
| Global explanation | Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images | Arxiv | ||
| Concepts based - Explainable by design | Inducing Semantic Grouping of Latent Concepts for Explanations: An Ante-Hoc Approach | Arxiv | IITH Vineeth sir group | |
| Explainable by design | This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation | Arxiv | ||
| MIL | ProtoMIL: Multiple Instance Learning with Prototypical Parts for Fine-Grained Interpretability | Arxiv | ||
| Concept based explanations | Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation | Arxiv | ||
| Counterfactual explanation + Theory of Mind | CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models | Arxiv | ||
| Evaluation metric | Counterfactual Evaluation for Explainable AI | Arxiv | ||
| CIM - FSC | CIM: Class-Irrelevant Mapping for Few-Shot Classification | Arxiv | ||
| Causal Concepts | Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation | Arxiv | ||
| ECE | Ensemble of Counterfactual Explainers | Paper | Code - seems hybrid of tf and torch | |
| Structured Explanations | From Heatmaps to Structured Explanations of Image Classifiers | Arxiv | ||
| XAI metric | An Objective Metric for Explainable AI - How and Why to Estimate the Degree of Explainability | Arxiv | ||
| DisCERN | DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods | Arxiv | ||
| PSEM | Towards Better Model Understanding with Path-Sufficient Explanations | Arxiv | Amit Dhurandhar sir group | |
| Evaluation traps | The Logic Traps in Evaluating Post-hoc Interpretations | Arxiv | ||
| Interactive explanations | Explainability Requires Interactivity | Arxiv | PyTorch | |
| CounterNet | CounterNet: End-to-End Training of Counterfactual Aware Predictions | Arxiv | PyTorch | |
| Evaluation metric - Concept based explanation | Detection Accuracy for Evaluating Compositional Explanations of Units | Arxiv | ||
| Explanation - Uncertainity | Effects of Uncertainty on the Quality of Feature Importance Explanations | Arxiv | ||
| Survey Paper | TOWARDS USER-CENTRIC EXPLANATIONS FOR EXPLAINABLE MODELS: A REVIEW | JISTM Journal Paper | ||
| Feature attribution | The Struggles and Subjectivity of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets | AAAI 2021 workshop | ||
| Contextual explanation | Context-based image explanations for deep neural networks | Image and Vision Computing Journal | ||
| Causal + Counterfactual | Counterfactual Instances Explain Little | Arxiv | ||
| Case based Posthoc | Explaining Deep Learning using examples: Optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI | Elsevier | ||
| Debugging gray box model | Toward a Unified Framework for Debugging Gray-box Models | Arxiv | ||
| Explainable by design | Optimising for Interpretability: Convolutional Dynamic Alignment Networks | Arxiv | ||
| XAI negative effect | Explainability Pitfalls: Beyond Dark Patterns in Explainable AI | Arxiv | ||
| Evaluate attributions | WHO EXPLAINS THE EXPLANATION? QUANTITATIVELY ASSESSING FEATURE ATTRIBUTION METHODS | Arxiv | ||
| Counterfactual explanations | Designing Counterfactual Generators using Deep Model Inversion | Arxiv | ||
| Model correction using explanation | Consistent Explanations by Contrastive Learning | Arxiv | ||
| Visualize feature maps | Visualizing Feature Maps for Model Selection in Convolutional Neural Networks | ICCV 2021 Workshop | Tensorflow 1.15 | |
| SPS | Stochastic Partial Swap: Enhanced Model Generalization and Interpretability for Fine-grained Recognition | ICCV 2021 | PyTorch | |
| DMBP | Generating Attribution Maps with Disentangled Masked Backpropagation | ICCV 2021 | ||
| Better CAM | Towards Better Explanations of Class Activation Mapping | ICCV 2021 | ||
| LEG | Statistically Consistent Saliency Estimation | ICCV 2021 | Keras | |
| IBA | Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information | NeurIPS 2021 | PyTorch | |
| Looks similar to This Looks Like That | Interpretable Image Recognition by Constructing Transparent Embedding Space | ICCV 2021 | Code not yet publicly released | |
| Causal Imagenet | CAUSAL IMAGENET: HOW TO DISCOVER SPURIOUS FEATURES IN DEEP LEARNING? | Arxiv | ||
| Model correction | Logic Constraints to Feature Importances | Arxiv | ||
| Receptive field Misalignment CAM | On the Receptive Field Misalignment in CAM-based Visual Explanations | Pattern recognition Letters | PyTorch | |
| Simplex | Explaining Latent Representations with a Corpus of Examples | Arxiv | PyTorch | |
| Sanity checks | Revisiting Sanity Checks for Saliency Maps | Arxiv - NeurIPS 2021 workshop | ||
| Model correction | Debugging the Internals of Convolutional Networks | |||
| SITE | Self-Interpretable Model with Transformation Equivariant Interpretation | Arxiv | Accepted at NeurIPS 2021 | EbD |
| Influential examples | Revisiting Methods for Finding Influential Examples | Arxiv | ||
| SOBOL | Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis | NeurIPS 2021 | Tensorflow and PyTorch | |
| Feature vectors | Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics | Arxiv | global interpretability | |
| OOD in explainability | The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations | NeurIPS 2021 | sklearn | |
| RPS LJE | Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models | NeurIPS 2021 | PyTorch | |
| Model correction | Editing a Classifier by Rewriting Its Prediction Rules | NeurIPS 2021 | Code | |
| suppressor variable litmus test | Scrutinizing XAI using linear ground-truth data with suppressor variables | Arxiv | ||
| Explainable knowledge distillation | Learning Interpretation with Explainable Knowledge Distillation | Arxiv | ||
| STEEX | STEEX: Steering Counterfactual Explanations with Semantics | Arxiv | Code | |
| Binary counterfactual explanation | Counterfactual Explanations via Latent Space Projection and Interpolation | Arxiv | ||
| ECLAIRE | Efficient Decompositional Rule Extraction for Deep Neural Networks | Arxiv | R | |
| CartoonX | Cartoon Explanations of Image Classifiers | Researchgate | ||
| concept based explanation | Explanations in terms of Hierarchically organised Middle Level Features | Paper | see how close to MACE and PACE | |
| Concept ball | Ontology-based 𝑛-ball Concept Embeddings Informing Few-shot Image Classification | Paper | ||
| SPARROW | SPARROW: Semantically Coherent Prototypes for Image Classification | BMVC 2021 | ||
| XAI evaluation criteria | Objective criteria for explanations of machine learning models | Paper | ||
| Code inversion with human perception | EXPLORING ALIGNMENT OF REPRESENTATIONS WITH HUMAN PERCEPTION | Arxiv | ||
| Deformable ProtoPNet | Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes | Arxiv | ||
| ICSN | Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations | Arxiv | ||
| HIVE | HIVE: Evaluating the Human Interpretability of Visual Explanations | Arxiv | Project Page | |
| Jitter CAM | Jitter-CAM: Improving the Spatial Resolution of CAM-Based Explanations | BMVC 2021 | PyTorch | |
| Interpreting last layer | dentifying Class Specific Filters with L1 Norm Frequency Histograms in Deep CNNs | Arxiv | ||
| FCP | Forward Composition Propagation for Explainable Neural Reasoning | Arxiv | ||
| Protopool | Interpretable Image Classification with Differentiable Prototypes Assignment | Arxiv | ||
| PRELIM | Pedagogical Rule Extraction for Learning Interpretable Models | Arxiv | ||
| Fair correction vectors | FAIR INTERPRETABLE LEARNING VIA CORRECTION VECTORS | ICLR 2021 | ||
| Smooth LRP | SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations | ESANN 2021 | ||
| Causal CAM | EXTRACTING CAUSAL VISUAL FEATURES FOR LIMITED LABEL CLASSIFICATION | ICIP 2021 |
| Title | Paper Title | Source Link | Code | Tags |
|---|---|---|---|---|
| SNI | Semantic Network Interpretation | WACV 2022 | ||
| F-CAM | F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling | WACV 2022 | PyTorch | |
| PCACE | PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability | Arxiv | ||
| Evaluating Attribution methods | Evaluating Attribution Methods in Machine Learning Interpretability | IEEE International Conference on Big Data | ||
| X-decision making | Explainable Decision Making with Lean and Argumentative Explanations | Arxiv | ||
| Include domain knowledge to neural network | A review of some techniques for inclusion of domain‑knowledge into deep neural networks | Nature | ||
| CNN Hierarchical Decomposition | Deeply Explain CNN via Hierarchical Decomposition | Arxiv | ||
| Explanatory learning | EXPLANATORY LEARNING: BEYOND EMPIRICISM IN NEURAL NETWORKS | Arxiv | ||
| Conceptor CAM | Conceptor Learning for Class Activation Mapping | IEEE-TIP | ||
| Classifier orthogonalization | CONTROLLING DIRECTIONS ORTHOGONAL TO A CLASSIFIER | ICLR 2022 | PyTorch | |
| Attention not explanation | Attention cannot be an Explanation | Arxiv | ||
| CNN sensitivity analysis | A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes | Arxiv | ||
| Trusting extrapolation | To what extent should we trust AI models when they extrapolate? | Arxiv | ||
| LAP | LAP: An Attention-Based Module for Faithful Interpretation and Knowledge Injection in Convolutional Neural Networks | Arxiv | concept based explanations | |
| Saliency map evaluation metrics | Metrics for saliency map evaluation of deep learning explanation methods | Arxiv | ||
| LINEX | Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning | Arxiv | ||
| ROAD | Evaluating Feature Attribution: An Information-Theoretic Perspective | Arxiv | PyTorch | |
| CBM-AUC | Concept Bottleneck Model with Additional Unsupervised Concepts | Arxiv | ||
| Explainability as dialogue | Rethinking Explainability as a Dialogue: A Practitioner’s Perspective | Arxiv | ||
| IAA | Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment | Arxiv | ||
| Plug in | A Novel Plug-in Module for Fine-Grained Visual Classification | Arxiv | PyTorch | |
| Hierarchical concepts | Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks | Arxiv | ||
| Model correction by design | LEARNING ROBUST CONVOLUTIONAL NEURAL NETWORKS WITH RELEVANT FEATURE FOCUSING VIA EXPLANATIONS | Arxiv | ||
| Concept discovery | Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization | Arxiv | ||
| Rare spurious correlation | Understanding Rare Spurious Correlations in Neural Networks | Arxiv | PyTorch | |
| Causal | Matching Learned Causal Effects of Neural Networks with Domain Priors | Arxiv | ||
| PYLON | Improved image classification explainability with high accuracy heatmaps | iScience Journal | ||
| Causal counterfactual | REALISTIC COUNTERFACTUAL EXPLANATIONS BY LEARNED RELATIONS | Arxiv | ||
| Argumentative Causal explanation | Forging Argumentative Explanations from Causal Models | Paper | ||
| EVA | Don’t Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis | Arxiv | ||
| Conceptual modelling | ConceptSuperimposition: Using Conceptual Modeling Method for Explainable AI | Paper | ||
| SIDU | Visual Explanation of Black-Box Model : Similarity Difference and Uniqueness (SIDU) Method | Pattern Recognition Journal | Tensorflow 2.x | |
| Explainable representations | Explaining, Evaluating and Enhancing Neural Networks’ Learned Representations | Arxiv | ||
| XAI Overview | Explanatory Paradigms in Neural Networks | Arxiv | ||
| Evaluating attribution methods | Evaluating Feature Attribution Methods in the Image Domain | Arxiv | PyTorch | |
| Prototype vector + perturbation | The Need for Empirical Evaluation of Explanation Quality | Arxiv | ||
| ADVISE | ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks | Arxiv | Matlab | |
| Improving Grad CAM | Improving the Interpretability of GradCAMs in Deep Classification Networks | Science Direct | ||
| Explainable by design | Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks | CVPR 2022 | PyTorch | |
| CAMP | Do Explanations Explain? Model Knows Best | Arxiv | PyTorch | |
| Attribution stability | RETHINKING STABILITY FOR ATTRIBUTION-BASED EXPLANATIONS | Arxiv | ||
| SSSCD | Sparse Subspace Clustering for Concept Discovery (SSCCD) | Arxiv | ||
| Model improvement | Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement | Arxiv | ||
| Causal explanations | Trying to Outrun Causality in Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables | Arxiv | sklearn | |
| Causal explanations | Diffusion Causal Models for Counterfactual Estimation | Arxiv | ||
| Causal inference influence functions | A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics | Arxiv | PyTorch | |
| Causal discovery | Causal discovery for observational sciences using supervised machine learning | Arxiv | ||
| Causal DA | Causal Domain Adaptation with Copula Entropy based Conditional Independence Test | Arxiv | ||
| Causal experimental design | Interventions, Where and How? Experimental Design for Causal Models at Scale | Arxiv | seems ICML format | |
| Causal discovery | SCORE MATCHING ENABLES CAUSAL DISCOVERY OF NONLINEAR ADDITIVE NOISE MODELS | Arxiv | ||
| Causal Explanation - Cynthia Rudin | WHY INTERPRETABLE CAUSAL INFERENCE IS IMPORTANT FOR HIGH-STAKES DECISION MAKING FOR CRITICALLY ILL PATIENTS AND HOW TO DO IT | Arxiv | ||
| Semantically consistent counterfactuals | Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals | Arxiv | ||
| Posthoc global hypersphere | Post-hoc Global Explanation using Hypersphere Sets | ICAART 2022 | ||
| CapsNet explanation | Investigation of Capsule Networks Regarding their Potential of Explainability and Image Rankings | ICAART 2022 | ||
| XAI evaluation | A Unified Study of Machine Learning Explanation Evaluation Metrics | Arxiv | ||
| Concept based counterfactual explanations | DISSECT: Disentangled Simultaneous Explanations via Concept Traversals | ICLR 2022 | tensorflow 1.12 | Been Kim's group |
| concept evolution | ConceptEvo: Interpreting Concept Evolution in Deep Learning Training | Arxiv | ||
| Poly-CAM | Backward recursive Class Activation Map refinement for high resolution saliency map | Paper | ||
| Interactive Concept explanation | ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective | Arxiv | ||
| Quasi ProtoPNet | Think positive: An interpretable neural network for image recognition | Neural Networks Journal | ||
| TAM | VISUALIZING DEEP NEURAL NETWORKS WITH TOPOGRAPHIC ACTIVATION MAPS | Arxiv | ||
| S-XAI | Semantic interpretation for convolutional neural networks: What makes a cat a cat? | Arxiv | ||
| See through DNN | Perception Visualization: Seeing Through the Eyes of a DNN | Arxiv | ||
| IOM | Understanding CNNs from excitations | Arxiv | ||
| KICE | Integrating Prior Knowledge in Post-hoc Explanations | Arxiv |
