SSL-ProtoNet: Self-supervised Learning Prototypical Networks for Few-shot Learning

This repository contains the pytorch code for the paper: "SSL-ProtoNet: Self-supervised Learning Prototypical Networks for Few-shot Learning" Jit Yan Lim, Kian Ming Lim, Chin Poo Lee, Yong Xuan Tan

Environment

The code is tested on Windows 10 with Anaconda3 and following packages:

• python 3.7.4
• pytorch 1.3.1

Preparation

1. Change the ROOT_PATH value in the following files to yours:

   • datasets/mini_imagenet.py
   • datasets/tiered_imagenet.py
   • datasets/cifarfs.py

2. Download the datasets and put them into corresponding folders that mentioned in the ROOT_PATH:
   

   • miniImageNet: download from CSS and put in data/mini-imagenet folder.

   • tieredImageNet: download from MetaOptNet and put in data/tiered-imagenet folder.

   • CIFARFS: download from MetaOptNet and put in data/cifar-fs folder.

Pre-trained Models

[Optional] The pre-trained models can be downloaded from here. Extract and put the content in the save folder. To evaluate the model, run the test.py file with the proper save path as in the next section.

Experiments

To train on 1-shot and 5-shot CIFAR-FS:

python train_stage1.py --dataset cifarfs --train-way 50 --train-batch 100 --save-path ./save/cifarfs-stage1

python train_stage2.py --dataset cifarfs --shot 1 --save-path ./save/cifarfs-stage2-1s --stage1-path ./save/cifarfs-stage1 --train-way 20
python train_stage2.py --dataset cifarfs --shot 5 --save-path ./save/cifarfs-stage2-5s --stage1-path ./save/cifarfs-stage1 --train-way 10

python train_stage3.py --kd-coef 0.7 --dataset cifarfs --shot 1 --train-way 20 --stage1-path ./save/cifarfs-stage1 --stage2-path ./save/cifarfs-stage2-1s --save-path ./save/cifarfs-stage3-1s
python train_stage3.py --kd-coef 0.1 --dataset cifarfs --shot 5 --train-way 10 --stage1-path ./save/cifarfs-stage1 --stage2-path ./save/cifarfs-stage2-5s --save-path ./save/cifarfs-stage3-5s

To evaluate on 5-way 1-shot and 5-way 5-shot CIFAR-FS:

python test.py --dataset cifarfs --shot 1 --save-path ./save/cifarfs-stage3-1s
python test.py --dataset cifarfs --shot 5 --save-path ./save/cifarfs-stage3-1s

Citation

If you find this repo useful for your research, please consider citing the paper:

@article{LIM2023122173,
  title = {SSL-ProtoNet: Self-supervised Learning Prototypical Networks for few-shot learning},
  journal = {Expert Systems with Applications},
  pages = {122173},
  year = {2023},
  issn = {0957-4174},
  doi = {https://doi.org/10.1016/j.eswa.2023.122173},
  author = {Jit Yan Lim and Kian Ming Lim and Chin Poo Lee and Yong Xuan Tan}
}

Contacts

For any questions, please contact:

Jit Yan Lim (lim.jityan@mmu.edu.my)
Kian Ming Lim (kmlim@mmu.edu.my)

Acknowlegements

This repo is based on Prototypical Networks, RFS, and SKD.