Added AGEM paper

README.md

@@ -5,6 +5,7 @@ I am trying a new initiative - a-paper-a-week. This repository will hold all tho
 
 ## List of papers
 
+* [Efficient Lifelong Learningi with A-GEM](https://shagunsodhani.in/papers-I-read/Efficient-Lifelong-Learning-with-A-GEM)
 * [Pre-training Graph Neural Networks with Kernels](https://shagunsodhani.in/papers-I-read/Pre-training-Graph-Neural-Networks-with-Kernels)
 * [Smooth Loss Functions for Deep Top-k Classification](https://shagunsodhani.in/papers-I-read/Smooth-Loss-Functions-for-Deep-Top-k-Classification)
 * [Hindsight Experience Replay](https://shagunsodhani.in/papers-I-read/Hindsight-Experience-Replay)

site/_posts/2018-05-21-Net2Net - Accelerating Learning via Knowledge Transfer.md

@@ -3,7 +3,7 @@ layout: post
 title: Net2Net-Accelerating Learning via Knowledge Transfer
 comments: True
 excerpt: The paper presents a simple yet effective approach for transferring knowledge from a trained neural network to a large, untrained network
-tags: ['2016', 'ICLR 2016', 'Knowledge Transfer', 'Life Long Learning', AI, CV]
+tags: ['2016', 'ICLR 2016', 'Knowledge Transfer', 'Lifelong Learning', AI, CV]
 ---
 
 ## Notes

site/_posts/2019-01-08-Efficient Lifelong Learning with A-GEM.md

@@ -0,0 +1,105 @@
+---
+layout: post
+title: Efficient Lifelong Learning with A-GEM
+comments: True
+excerpt: 
+tags: ['2019', 'Catastrophic Forgetting', 'Continual Learning', 'ICLR 2019', 'Lifelong Learning', AI, CV, CL, ICLR]
+---
+
+## Contributions
+
+* A new (and more realistic) evaluation protocol for lifelong learning where each data point is observed just once and a disjoint set of tasks are used for training and validation.
+
+* A new metric that focuses on the efficiency of the models - in terms of sample complexity and computational (and memory) costs.
+
+* Modification of [Gradient Episodic Memory ie GEM](https://arxiv.org/abs/1706.08840) which reduces the computational overhead of GEM without compromising on the results.
+
+* Empirical validation that using task descriptors help lifelong learning models and improve their few-shot learning capabilities.
+
+* [Link to the paper](https://arxiv.org/abs/1812.00420)
+
+* [Link to the code](https://github.com/facebookresearch/agem/)
+
+## Learning Protocol
+
+* Two group of datasets - one for training and evaluation (D<sup>EV</sup>) and other for cross validation (D<sup>CV</sup>).
+
+* Data can be sampled multiple times for cross-validation dataset but only once from the training dataset.
+
+* Each group of dataset (say D<sup>EV</sup> or D<sup>CV</sup>) is a list of task-specific datasets D<sub>k</sub> (k is the task index).
+
+* Each sample in D<sub>k</sub> is of the form (x, t, y) where x is the data, t is the task descriptor and y is the output.
+
+* D<sub>k</sub> contains B<sup>k</sup> minibatches of data.
+
+## Metrics
+
+### Accuracy
+
+* a<sub>k,i,j</sub> = accuracy on test task j after training on ith minibatch of training task k.
+
+* A<sub>k</sub> = mean over all j = 1 to k (a<sub>k, B<sub>k</sub>, j</sub>) ie train the model on data for task k and then test it on all the tasks.
+
+### Forgetting Measure
+
+* f<sub>j</sub><sup>k</sup> = forgetting on task j after training on all minibatches upto task k.
+
+* f<sub>j</sub><sup>k</sup> = max over all l = 1 to k-1 (a<sub>l, B<sub>l</sub>j</sub> - a<sub>k, B<sub>k</sub>j</sub>)
+
+* Forgetting = F<sub>k</sub> = mean over all j = 1 to k-1 (f<sub>j</sub><sup>k</sup>)
+
+### LCA - Learning Curve Area
+
+* Z<sub>b</sub> = average b shot performance where b is the minibatch number.
+
+* Z<sub>b</sub> = mean over all k = 0 to T (a<sub>k, b, k</sub>)
+
+* LCA<sub>&beta;</sub> = mean over all b = 0 to &beta; (Z<sub>b</sub>)
+
+* One special case is LCA<sub>0</sub> which is the forward transfer performance or performance on the unseen task.
+
+* In experiments, &beta; is kept small as we want the model to learn from few examples.
+
+## Model
+
+* GEM has been shown to be very effective in single epoch setting but introduces a very high computational overhead.
+
+* Average GEM (AGEM) reduces this overhead by sampling (and using) only some examples from the episodic memory instead of using all the examples.
+
+* While GEM provides better guarantees in terms of worst-case forgetting, AGEM provides better guarantees in terms of average accuracy.
+
+## Joint Embedding Model Using Compositional Task Descriptors
+
+* Compositional Task Descriptors are used to speed training on the subsequent tasks.
+
+* A matrix specifying the attribute value of objects (to be recognized in the task) are used.
+
+* A joint-embedding space between image features and attribute embeddings is learned.
+
+## Experiments
+
+### Datasets
+
+* [Permuted MNIST](https://arxiv.org/abs/1612.00796)
+
+* [Split CIFAR](https://arxiv.org/abs/1703.04200)
+
+* [Split CUB](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html)
+
+* [Split AWA](http://cvml.ist.ac.at/papers/lampert-cvpr2009.pdf)
+
+### Setup
+
+* Integer task descriptors for MNIST and CIFAR and class attributes as descriptors for CUB and AWA
+
+* Baselines include [GEM](https://arxiv.org/abs/1706.08840), [iCaRL](https://arxiv.org/abs/1611.07725), [Elastic Weight Consolidation](https://arxiv.org/pdf/1612.00796.pdf), [Progressive Neural Networks](https://arxiv.org/abs/1606.04671) etc.
+
+## Results
+
+* AGEM outperforms other models on all the datasets expect MNIST where the Progressive Neural Networks lead. One reason could be that MNIST has a large number of training examples per task. But Progressive Neural Networks lead to bad utilization of capacity.
+
+* While AGEM and GEM have similar performance, GEM has a much higher computational and memory overhead.
+
+* Use of task descriptors improves the accuracy for all the models.
+
+* It seems that AGEM offers a good tradeoff between average accuracy performance and efficiency - in terms of sample efficiency, memory requirements and computational costs.