The official PyTorch implementation of papers:
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HyperShot: Few-Shot Learning by Kernel HyperNetworks (2023) Sendera M., Przewięźlikowski M., Karanowski K., Zięba M. Tabor J., Spurek P. - WACV 2023.
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The general framework for few-shot learning by kernel HyperNetworks (2023) Sendera M., Przewięźlikowski M., Miksa J., Rajski M., Karanowski K., Zięba M. Tabor J., Spurek P. - Machine Vision and Applications Volume 34, article number 53
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HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks (2022) Przewięźlikowski M., Przybysz P. , Tabor J., Zięba M., Spurek P. - preprint.
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Hypernetwork approach to Bayesian MAML (2022) Borycki P., Kubacki P., Przewięźlikowski M., Kuśmierczyk T., Tabor J., Spurek P., preprint.
Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting where only one element represents each class. We propose HyperShot - the fusion of kernels and hypernetwork paradigm. Compared to reference approaches that apply a gradient-based adjustment of the parameters, our model aims to switch the classification module parameters depending on the task's embedding. In practice, we utilize a hypernetwork, which takes the aggregated information from support data and returns the classifier's parameters handcrafted for the considered problem. Moreover, we introduce the kernel-based representation of the support examples delivered to hypernetwork to create the parameters of the classification module. Consequently, we rely on relations between embeddings of the support examples instead of direct feature values provided by the backbone models. Thanks to this approach, our model can adapt to highly different tasks.
While HyperShot obtains very good results, it is limited by typical problems such as poorly quantified uncertainty due to limited data size. We further show that incorporating Bayesian neural networks into our general framework, an approach we call BayesHyperShot, solves this issue.
The aim of Few-Shot learning methods is to train models which can easily adapt to previously unseen tasks, based on small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). The main idea behind this method is to learn the general weights of the meta-model, which are further adapted to specific problems in a small number of gradient steps. However, the model's main limitation lies in the fact that the update procedure is realized by gradient-based optimisation. In consequence, MAML cannot always modify weights to the essential level in one or even a few gradient iterations. On the other hand, using many gradient steps results in a complex and time-consuming optimization procedure, which is hard to train in practice, and may lead to overfitting. In this paper, we propose HyperMAML, a novel generalization of MAML, where the training of the update procedure is also part of the model. Namely, in HyperMAML, instead of updating the weights with gradient descent, we use for this purpose a trainable Hypernetwork. Consequently, in this framework, the model can generate significant updates whose range is not limited to a fixed number of gradient steps. Experiments show that HyperMAML consistently outperforms MAML and performs comparably to other state-of-the-art techniques in a number of standard Few-Shot learning benchmarks.
The main goal of Few-Shot learning algorithms is to enable learning from small amounts of data. One of the most popular and elegant Few-Shot learning ap- proaches is Model-Agnostic Meta-Learning (MAML). The main idea behind this method is to learn shared universal weights of a meta-model, which then are adapted for specific tasks. However, due to limited data size, the method suffers from over-fitting and poorly quantifies uncertainty. Bayesian approaches could, in principle, alleviate these shortcomings by learning weight distributions in place of point-wise weights. Unfortunately, previous Bayesian modifications of MAML are limited in a way similar to the classic MAML, e.g., task-specific adaptations must share the same structure and can not diverge much from the universal meta-model. Additionally, task-specific distributions are considered as posteriors to the universal distributions working as priors and optimizing them jointly with gradients is hard and poses a risk of getting stuck in local optima. In this paper, we propose BayesHMAML, a novel generalization of Bayesian MAML, which employs Bayesian principles along with Hypernetworks for MAML. We achieve better convergence than the previous methods by classically learning universal weights. Furthermore, Bayesian treatment of the specific tasks enables uncertainty quantification, and high flexibility of task adaptations is achieved using Hypernetworks instead of gradient-based updates. Consequently, the proposed approach not only improves over the previous methods, both classic and Bayesian MAML in several standard Few-Shot learning benchmarks but also benefits from the properties of the Bayesian framework.
- Python >= 3.7
- Numpy >= 1.19
- pyTorch >= 1.11
- GPyTorch >= 1.5.1
- (optional) neptune-client for logging traning results into your Neptune project.
pip install numpy torch torchvision gpytorch h5py pillow
- (Bayesian) HyperShot: hypernet_kernel.py
- HyperMAML: hypermaml.py
- BayesHMAML bayeshmaml.py
You can run
python train.py --h to list all the possible arguments.
The train.py script performs the whole training, evaluation and final test procedure.
For re-running our experiments, please refer to the commands.sh file.
Check out commands.sh for our best grid test arguments
This repository provides implementations of several few-shot learning methods:
hyper_shot- HyperShot: Few-Shot Learning by Kernel HyperNetworks / The general framework for few-shot learning by kernel HyperNetworkshyper_maml- HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworksbayes_hmaml- Hypernetwork approach to Bayesian MAMLhn_ppa- Few-Shot Image Recognition by Predicting Parameters from ActivationsDKT- Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernelsmaml,maml_approx- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networksprotonet- Prototypical Networks for Few-shot Learningrelationnet- Learning to Compare: Relation Network for Few-Shot Learningmatchingnet- Matching Networks for One Shot Learningbaseline++- A Closer Look at Few-Shot Classificationbaseline- Feature Transfer
You must use those exact strings at training and test time when you call the script (see below).
This is an example of how to download and prepare a dataset for training/testing. Here we assume the current directory is the project root folder:
cd filelists/DATASET_NAME/
sh download_DATASET_NAME.sh
Replace DATASET_NAME with one of the following: omniglot, CUB, miniImagenet, emnist, QMUL. Notice that mini-ImageNet is a large dataset that requires substantial storage, therefore you can save the dataset in another location and then change the entry in configs.py in accordance.
These are the instructions to train and test the methods reported in the paper in the various conditions.
In addition, you can select cross_char and cross datasets for cross-domain classification of
Omnglot → EMNIST and mini-ImageNet → CUB, respectively.
The script allows training and testing on different backbone networks. By default the script will use the same backbone used in our experiments (Conv4). Check the file backbone.py for the available architectures, and use the parameter --model=BACKBONE_STRING where BACKBONE_STRING is one of the following: Conv4, Conv6, ResNet10|18|34|50|101.
We provide logging the training / validation metrics and details to Neptune. To do so, one must export the following env variables before running train.py.
export NEPTUNE_PROJECT=...
export NEPTUNE_API_TOKEN=...This repository is a fork of: https://github.com/BayesWatch/deep-kernel-transfer, which in turn is a fork of https://github.com/wyharveychen/CloserLookFewShot.
If you find our work useful, please consider citing it:
@InProceedings{sendera2023hypershot,
author = {Sendera, Marcin and Przewi\k{e}\'zlikowski, Marcin and Karanowski, Konrad and Zi\k{e}ba, Maciej and Tabor, Jacek and Spurek, Przemys{\l}aw},
title = {HyperShot: Few-Shot Learning by Kernel HyperNetworks},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023},
pages = {2469-2478}
}@article{sendera2023thegeneral,
author = {Sendera, Marcin and Przewięźlikowski, Marcin and Miksa, Jan and Rajski, Mateusz and Karanowski, Konrad and Zięba, Maciej and Tabor, Jacek and Spurek, Przemysław},
year = {2023},
month = {05},
pages = {},
title = {The general framework for few-shot learning by kernel HyperNetworks},
volume = {34},
journal = {Machine Vision and Applications},
doi = {10.1007/s00138-023-01403-4}
}@misc{przewiezlikowski2022hypermaml,
doi = {10.48550/ARXIV.2205.15745},
url = {https://arxiv.org/abs/2205.15745},
author = {Przewięźlikowski, M. and Przybysz, P. and Tabor, J. and Zięba, M. and Spurek, P.},
keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}@misc{borycki2023hypernetwork,
title={Hypernetwork approach to Bayesian MAML},
author={Piotr Borycki and Piotr Kubacki and Marcin Przewięźlikowski and Tomasz Kuśmierczyk and Jacek Tabor and Przemysław Spurek},
year={2023},
eprint={2210.02796},
archivePrefix={arXiv},
primaryClass={cs.LG}
}