P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification

Backbone Training

We use the same backbone network and training strategies as 'S2M2_R'. Please refer to https://github.com/nupurkmr9/S2M2_fewshot for the backbone training.

Extract and save features

After training the backbone as 'S2M2_R', extract features as below:

• Create an empty 'checkpoints' directory.

• Run:

python save_plk.py --dataset [miniImagenet/CUB] 

Or you can directly download the extracted features/pretrained models from the link:

https://drive.google.com/drive/folders/1IjqOYLRH0OwkMZo8Tp4EG02ltDppi61n?usp=sharing

After downloading the extracted features, please adjust your file path according to the code.

Evaluate our distribution calibration

To evaluate our P3DC-Shot method, run:

python P3DC.py for 1-shot
python P3DC_5shot.py for 5-shot

Reference

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

https://github.com/nupurkmr9/S2M2_fewshot

Free Lunch for Few-shot Learning: Distribution Calibration

https://github.com/ShuoYang-1998/Few_Shot_Distribution_Calibration