Code release for "Learning to Adapt to Evolving Domains" (NeurIPS 2020)
- PyTorch >= 0.4.0 (with suitable CUDA and CuDNN version)
- torchvision >= 0.2.1
- Python3
- Numpy
- argparse
- PIL
Rotated MNIST: https://drive.google.com/file/d/1eaw42sg4Cgm34790AW_SKGCSkFosugl2/view?usp=sharing
EAML
%run eaml.py rot_mnist_28/ --lip-balance 0.2 --lip-jth 0.01 --epochs 500 --lr-in 0.03 --lr-out 0.003
JAN
%run JAN.py rot_mnist_28/ --lip-balance 0.2 --lip-jth 0.01 --epochs 500 --lr-in 0.03 --lr-out 0.003
Source
%run source.py rot_mnist_28/ --lip-balance 0.2 --lip-jth 0.01 --epochs 500 --lr-out 0.003
This code is implemented based on the JAN (Joint Adaptation Networks) code, and it is our pleasure to acknowledge their contributions. The meta-learning code is adapted from https://github.com/dragen1860/MAML-Pytorch/.
If you use this code for your research, please consider citing:
@inproceedings{NEURIPS2020_fd69dbe2,
author = {Liu, Hong and Long, Mingsheng and Wang, Jianmin and Wang, Yu},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {22338--22348},
publisher = {Curran Associates, Inc.},
title = {Learning to Adapt to Evolving Domains},
url = {https://proceedings.neurips.cc/paper/2020/file/fd69dbe29f156a7ef876a40a94f65599-Paper.pdf},
volume = {33},
year = {2020}
}
If you have any problem about our code, feel free to contact