This project provides a strong Baseline with WRN28-10 and ResNet-10 backbone for the following Few-Shot Learning methods:
- Fine-tuning
- Matching Networks
- Model-Agnostic Meta-Learning (MAML)
- Meta-Transfer Learning for Few-Shot Learning (MTL)
- Meta-Learning with Latent Embedding Optimization (LEO)
- Synthetic Information Bottleneck (SIB)
This also includes implementation of our NeurIPS 2020 paper Interventional Few-Shot Learning, which proposes IFSL classifier based on intervention P(Y|do(X)) to remove the confounding bias from pre-trained knowledge. Our IFSL classifier is generally applicable to all fine-tuning and meta-learning method, easy to plug in and involves no additional training steps.
The codes are organized into four folders according to methods. The folder MAML_MN_FT contains baseline and IFSL for fine-tuning, Matching Networks and MAML.
Recommended version:
- Python 3.7.6
- PyTorch 1.4.0
- Download pre-trained backbone in https://github.com/mileyan/simple_shot
- Download mini-ImageNet following https://github.com/hushell/sib_meta_learn
- Tiered-ImageNet download instruction https://github.com/yaoyao-liu/meta-transfer-learning
- Download CUB from official website http://www.vision.caltech.edu/visipedia/CUB-200.html
After downloading the weights and datasets, you can follow the instructions in each folder to modify the code and finish preparation.
Apologize in advance for dirty code, which I will clean up gradually.
- Code refactoring
- Improve documentation and optimize project setup procedures
The implementation is based on the following repositories (for correctness of baseline, most of our code is based on the official released code).
- A Closer Look at Few Shot Learning: https://github.com/wyharveychen/CloserLookFewShot
- Synthetic Information Bottleneck: https://github.com/amzn/xfer/tree/master/synthetic_info_bottleneck
- Meta-Transfer Learning: https://github.com/yaoyao-liu/meta-transfer-learning
- Meta-Learning with Latent Embedding Optimization: https://github.com/deepmind/leo
- SimpleShot: https://github.com/mileyan/simple_shot
If you find our work or the code useful, please consider cite our paper using:
@inproceedings{yue2020interventional,
title={Interventional Few-Shot Learning},
author={Yue, Zhongqi and Zhang, Hanwang and Sun, Qianru and Hua, Xian-Sheng},
booktitle= {NeurIPS},
year={2020}
}
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