Efficient-PrototypicalNet with self knowledge distillation for few-shot learning

This repository contains the pytorch code for the paper: "Efficient-PrototypicalNet with self knowledge distillation for few-shot learning" Jit Yan Lim, Kian Ming Lim, Shih Yin Ooi, Chin Poo Lee

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 path value in the following files to yours:

   • datasets/mini_imagenet.py
   • datasets/tiered_imagenet.py
   • datasets/cifarfs.py
   • datasets/fc100.py
   • datasets/omniglot.py (only change the path in OmniglotFeatures class for extracted features)

2. [Optional] Download the original datasets and put them into corresponding folders:
   

   • miniImageNet: download from https://drive.google.com/open?id=0B3Irx3uQNoBMQ1FlNXJsZUdYWEE and put in data/miniImageNet/images folder.

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

   • CIFARFS: download from MetaOptNet and put in data/cifarfs folder.

   • FC100: download from MTL, extract them into train, val, and test folders and put in data/fc100 folder.

   • Omniglot: the download for Omniglot is automatic when the script executed.

Extract Features

1. The extracted features can be downloaded from here or

2. [Optional] The pretrained weights for EfficientNet-B0 can be downloaded from here: miniImageNet, tieredImageNet, and other datasets. Run the below command extract the feature vectors, NAME decide the dataset and PATH define the location to store the extracted features.
   

   python preprocess.py --dataset NAME --save-path PATH
   

Experiments

To train on 30-way 1-shot miniImageNet:

python train.py --dataset mini --train-way 30 --shot 1 --save-path ./save/mini_30w1s

To train the 30-way 1-shot miniImageNet with SSKD:

python train_distill.py --dataset mini --train-way 30 --shot 1 --pretrain-path ./save/mini_30w1s --save-path ./save/mini_30w1s_distill

To evaluate on 5-way 1-shot miniImageNet:

python evaluate.py --dataset mini --test-way 5 --shot 1 --save-path ./save/mini_30w1s_distill

To train on N-way 5-shot Omniglot:

python omniglot_train.py --shot 5 --mode 0
python omniglot_train_distill.py --shot 5 --mode 0

To evaluate on N-way 5-shot Omniglot:

python omniglot_train_distill.py --shot 5 --mode 1

Citation

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

@article{LIM2021327,
  title = {Efficient-PrototypicalNet with self knowledge distillation for few-shot learning},
  journal = {Neurocomputing},
  volume = {459},
  pages = {327-337},
  year = {2021},
  issn = {0925-2312},
  doi = {https://doi.org/10.1016/j.neucom.2021.06.090},
  url = {https://www.sciencedirect.com/science/article/pii/S0925231221010262},
  author = {Jit Yan Lim and Kian Ming Lim and Shih Yin Ooi and Chin Poo Lee}
}

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, EfficientNet, MetaOptNet, and MTL.