Kernel Relative-prototype Spectral Filtering for Few-shot Learning

This repository contains the code for the paper: Kernel Relative-prototype Spectral Filtering for Few-shot Learning

Citation:

@inproceedings{zhang2022kernel,
title={Kernel Relative-prototype Spectral Filtering for Few-shot Learning},
author={Zhang, Tao and Huang, Wu},
booktitle={European Conference on Computer Vision},
year={2022}
organization={Springer}
}

Dependencies:

• Python 3.9.7
• PyTorch 1.11.0
• qpth 0.0.15
• tqdm

Dataset:

miniImageNet, tieredImageNet, CIFAR-FS

Training:

1. Train shrinkage classifier using ResNet-12 on 5-way mini-ImageNet 5-shot/1-shot benchmark:

   python train.py --gpu 0,1,2,3 --save-path "./experiments/miniImageNet_shrinkage" --train-shot 15 --train-query 10\
   --head shrinkage --network ResNet --dataset miniImageNet --eps 0.1

2. Train shrinkage classifier using Conv-4 on 5-way mini-ImageNet 5-shot/1-shot benchmark:

   python train.py --gpu 0 --save-path "./experiments/miniImageNet_shrinkage" --train-shot 15 --train-query 10\
   --head shrinkage --network ProtoNet --dataset miniImageNet

3. Train shrinkage classifier using ResNet-12 on 5-way tieredImageNet 5-shot/1-shot benchmark:

   python train.py --gpu 0,1,2,3 --save-path "./experiments/tieredImageNet_shrinkage" --train-shot 10 --train-query 15\
   --head shrinkage --network ResNet --dataset tieredImageNet

4. Train shrinkage classifier using ResNet-12 on 5-way CIFAR-FS 1-shot benchmark:

   python train.py --gpu 0 --save-path "./experiments/CIFAR_FS_shrinkage" --train-shot 2 --train-query 20\
   --head shrinkage --network ResNet --dataset CIFAR_FS

5. Train shrinkage classifier using ResNet-12 on 5-way CIFAR-FS 5-shot benchmark:

   python train.py --gpu 0 --save-path "./experiments/CIFAR_FS_shrinkage" --train-shot 15 --train-query 10\
   --head shrinkage --network ResNet --dataset CIFAR_FS

Testing:

1. Test shrinkage classifier with ResNet-12 on 5-way miniImageNet 5-shot benchmark:

python test.py --gpu 0 --load ./experiments/miniImageNet_shrinkage/best_model.pth --episode 1000 \
--way 5 --shot 5 --query 15 --head shrinkage --network ResNet --dataset miniImageNet

2. Train shrinkage classifier with ResNet-12 on 5-way miniImageNet 1-shot benchmark:

python test.py --gpu 0 --load ./experiments/miniImageNet_shrinkage/best_model.pth --episode 1000 \
--way 5 --shot 1 --query 15 --head shrinkage --network ResNet --dataset miniImageNet

Acknowledgement:

This code is based on the codebases:

Prototypical Network, MetaOpt, DSN