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Leveraging the Feature Distribution in Transfer-based Few-Shot Learning

This repository is the official implementation of Leveraging the Feature Distribution in Transfer-based Few-Shot Learning.

Requirements

To install requirements:

pip install -r requirements.txt

Donwloading the dataset and create base/val/novel splits:

miniImageNet

• Change directory to filelists/miniImagenet/
• Run 'source ./download_miniImagenet.sh'

CUB

• Change directory to filelists/CUB/
• Run 'source ./download_CUB.sh'

CIFAR-FS

• Download CIFAR-FS
• Decompress and change the filename to 'cifar-FS'
• Move the datafile to filelists/cifar/
• Run 'python write_cifar_filelist.py'

Training

To train the feature extractors in the paper, run this command:

For miniImageNet/CUB<br/>

python train.py --dataset [miniImagenet/CUB] --method [S2M2_R/rotation] --model [WideResNet28_10/ResNet18] --train_aug

For CIFAR-FS<br/>

python train_cifar.py --dataset cifar --method [S2M2_R/rotation] --model [WideResNet28_10/ResNet18] --train_aug

Evaluation

To evaluate my model on miniImageNet/CUB/cifar/cross, run:

python test_standard.py

Pre-trained Models

You can download pretrained models and extracted features here:

• trained models trained on miniImageNet, CUB and CIFAR-FS using WRN.

• Create an empty 'checkpoints' directory.

• Untar the downloaded file and move it into 'checkpoints' folder.

📋 To extract and save the novel class features of a newly trained backbone, run:

python save_plk.py --dataset [miniImagenet/CUB] --method S2M2_R --model [trainedmodel]

Results

Our model achieves the following performance (backbone: WRN) on :

References

A Closer Look at Few-shot Classification

Charting the Right Manifold: Manifold Mixup for Few-shot Learning

Manifold Mixup: Better Representations by Interpolating Hidden States

Sinkhorn Distances: Lightspeed Computation of Optimal Transport

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

Notes on optimal transport