Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
SPPR
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
This is the implementation of the paper “Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning” (accepted to CVPR2021).
Requirements
- Python 3.8
- PyTorch 1.8.1 (>1.1.0)
- cuda 11.2
Preparing Few-Shot Class-Incremental Learning Datasets
Download following datasets:
1. CIFAR-100
Automatically downloaded on torchvision.
2. MiniImageNet
(1) Download MiniImageNet train/test images[github],
and prepare related datasets according to [TOPIC].(2) or Download processed data from our Google Drive: [mini-imagenet.zip],
(and locate the entire folder under datasets/ directory).
3. CUB200
(1) Download CUB200 train/test images, and prepare related datasets according to [TOPIC]:
wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz(2) or Download processed data from our Google Drive: [cub.zip],
(and locate the entire folder under datasets/ directory).