Awesome

This repo holds the code for SAMPTransfer

Offical repository for Self-Attention Message Passing for Contrastive Few-Shot Learning(Ojas Kishore Shirekar, Anuj Singh, Hadi Jamali-Rad)

This paper is accepted to appear in the proceedings of WACV 2023

Introduction

Humans have a unique ability to learn new representations from just a handful of examples with little to no supervision. Deep learning models, however, require a ton of data and supervision to perform at a satisfactory level. Unsupervised few-shot learning (U-FSL) is the pursuit of bridging this gap between machines and humans. Inspired by the capacity of graph neural networks (GNNs) in discovering complex inter-sample relationships, we propose a novel self-attention based message passing contrastive learning approach (coined as SAMP-CLR) for U-FSL pre-training. We also propose an optimal transport (OT) based fine-tuning strategy (we call OpT-Tune) to efficiently induce task awareness into our novel end-to-end unsupervised few-shot classification framework (SAMPTransfer). Our extensive experimental results corroborate the efficacy of SAMPTransfer in a variety of downstream few-shot classification scenarios, setting a new state-of-the-art for U-FSL on both miniImageNet and tieredImageNet benchmarks, offering up to 7%+ and 5%+ improvements, respectively. Our further investigations also confirm that SAMPTransfer remains on-par with some supervised baselines on miniImageNet and outperforms all existing U-FSL baselines in a challenging cross-domain scenario.

SAMP-CLR (Pre-Training)

OpT-Tune (Fine-Tuning)

Algorithms

SAMP-CLR	OpT-Tune

Requirements

The code base requires cuda to run at its best in terms of speed. This codebase uses Pytorch-Lightning and LightningCLI for executing runs

1. cd into the SAMPTransfer folder
2. Create a new conda environment by running: conda env create -f environment.yml
3. Install poetry: conda install -c conda-forge poetry
4. Run poetry install

Running Experiments

Datasets

1. Make sure all required packages are installed before running the experiments
2. Ensure that there is enough space for datasets to be downloaded - incase they haven't been downloaded already
   1. You can use download_data.py to have torchmeta download the data for you
   2. In the event the automatic downloader fails, please download the data from links given ** here**
3. CDFSL Benchmark download instructions are here

Training

1. Edit the datapath key in config file:configs/slurm/mpnn.yml to the location where your dataset is stored
2. To start a training run, simply execute python clr_gat.py -c configs/slurm/mpnn.yml
   1. You may edit the .yml file to reflect your hardware availability
3. This codebase uses Weights and Biases for all the logging needs, you may disable it from the config file if needed

Evaluation

Even though a testing run is triggered by the completion of training, you may want to test out the pre-trained checkpoint artifacts generated during training.

For this purpose, this codebase contains evaluator.py to make life easier. To use it modify the following command:

python evaluator.py <dataset> <path_to_checkpoint> <path_to_data> <n_ways> <k_shots> <query_shots> prototune --adapt ot --ft-freeze-backbone

Results

The results on mini-ImageNet and tieredImageNet are given below:

Citation

@misc{https://doi.org/10.48550/arxiv.2210.06339,
  doi = {10.48550/ARXIV.2210.06339},
  
  url = {https://arxiv.org/abs/2210.06339},
  
  author = {Shirekar, Ojas Kishorkumar and Singh, Anuj and Jamali-Rad, Hadi},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},

Contact

Corresponding author: Ojas Kishore Shirekar (ojas.shirekar@gmail.com)

References

1. Qin, T., Li, W., Shi, Y., & Gao, Y. (2020). Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation. Retrieved from https://github.com/WonderSeven/ULDA.
2. Zhang, H., Koniusz, P., Jian, S., Li, H., & Torr, P. H. S. (2020). Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning. Retrieved from http://arxiv.org/abs/2001.03919
3. Zhang, H., Li, H., & Koniusz, P. (2022). Multi-level Second-order Few-shot Learning. IEEE Transactions on Multimedia. https://doi.org/10.1109/TMM.2022.3142955
4. Medina, C., Devos, A., & Grossglauser, M. (2020). Self-Supervised Prototypical Transfer Learning for Few-Shot Classification. Retrieved from http://arxiv.org/abs/2006.11325
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6. Bok Lee, D., Min, D., Lee, S., Ju Hwang, S., & Korea, S. (n.d.). META-GMVAE: MIXTURE OF GAUSSIAN VAES FOR UNSUPERVISED META-LEARNING.
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8. Li, Jianyi, and Guizhong Liu. "Few-shot image classification via contrastive self-supervised learning." arXiv preprint arXiv:2008.09942 (2020).
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12. Ye, Han-Jia, et al. "Few-shot learning via embedding adaptation with set-to-set functions." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
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14. Bateni, Peyman, et al. "Enhancing few-shot image classification with unlabelled examples." Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022.
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