Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning

This repository is the official implementation of Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning.

Requirements

To install requirements:

pip install -r requirements.txt

Pre-trained Models

You can download the extracted features here:

• Extracted novel class features on miniImageNet, tieredImagenet, cub and cifar-fs .

• Create a 'checkpoint' folder

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

Boosted Min-size Sinkhorn

📋 To launch the BMS algorithm, run:

python test_standard_bms.py --dataset [mini/tiered/cub/cifar-fs] --model wrn --method [BMS/BMS_] --preprocess PEME --shot [1/5] --epoch [0/20/40] 

Results

Our model achieves the following performance on :

Dataset	1-shot Accuracy	5-shot Accuracy
miniImageNet	83.35+-0.25%	89.53+-0.13%
tieredImageNet	86.07+-0.25%	91.09+-0.14%
CUB	91.91+-0.18%	94.62+-0.09%
CIFAR-FS	87.83+-0.22%	91.20+-0.15%

References

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

Sinkhorn Distances: Lightspeed Computation of Optimal Transport

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

Notes on optimal transport