Zero-Base GFSL

Better Generalized Few-Shot Learning Even Without Base Data in AAAI 2023
by Seong-Woong Kim and Dong-Wan Choi

Running the code

Preliminaries

Environment

• Python 3.7.4
• Pytorch 1.9.0

Datasets

• miniImageNet (courtesy of Spyros Gidaris)
• tieredImageNet (courtesy of Kwonjoon Lee)
• ImageNet-800

Download the datasets and link the folders into materials/ with names mini-imagenet, tiered-imagenet and imagenet. Note imagenet refers to ILSVRC-2012 1K dataset with two directories train and val with class folders.

When running python programs, use --gpu to specify the GPUs for running the code (e.g. --gpu 0,1). For Classifier-Baseline, we train with 4 GPUs on miniImageNet and tieredImageNet and with 8 GPUs on ImageNet-800. Meta-Baseline uses half of the GPUs correspondingly.

In following we take miniImageNet as an example. For other datasets, replace mini with tiered or im800. By default it is 1-shot, modify shot in config file for other shots. Models are saved in save/.

1. Pre-training

python train_classifier.py --config configs/train_classifier_mini.yaml

(The pretrained model can be downloaded here)

2. Fine-tuning

To test the performance, modify configs/test_few_shot.yaml by setting load_encoder to the saving file of Pre-training.

E.g., load: ./save/mini-iamgenet/max-va.pth

Then run

python test_few_shot.py --shot 1

Advanced instructions

Configs

A dataset/model is constructed by its name and args in a config file.

For a dataset, if root_path is not specified, it is materials/{DATASET_NAME} by default.

For a model, to load it from a specific saving file, change load_encoder or load to the corresponding path. load_encoder refers to only loading its .encoder part.

Acknowledgment

Our code is based on the implementations of Meta-Baseline