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Code Repository for "SSL-ProtoNet: Self-supervised Learning Prototypical Networks for few-shot learning"

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# 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](https://doi.org/10.1016/j.eswa.2023.122173)" 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: 
 - ***mini*ImageNet**: download from [CSS](https://github.com/anyuexuan/CSS) and put in `data/mini-imagenet` folder.

- ***tiered*ImageNet**: download from [RFS](https://www.dropbox.com/sh/6yd1ygtyc3yd981/AABVeEqzC08YQv4UZk7lNHvya?dl=0) and put in `data/tiered-imagenet` folder.

- **CIFARFS**: download from [MetaOptNet](https://github.com/kjunelee/MetaOptNet) and put in `data/cifar-fs` folder.

## Pre-trained Models
[Optional] The pre-trained models can be downloaded from [here](https://drive.google.com/file/d/14IOHnVfVACpkhjj1o3ZjwG7YD4p6ULLM/view?usp=sharing). 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 (jityan95@gmail.com) 
Kian Ming Lim (Kian-Ming.Lim@nottingham.edu.cn)

## Acknowlegements
This repo is based on **[Prototypical Networks](https://github.com/yinboc/prototypical-network-pytorch)**, **[RFS](https://github.com/WangYueFt/rfs)**, and **[SKD](https://github.com/brjathu/SKD)**.

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