Skip to content
/ EAML Public

Code Release for Learning to Adapt to Evolving Domains

Notifications You must be signed in to change notification settings

Liuhong99/EAML

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

9 Commits
 
 
 
 

Repository files navigation

EAML

Code release for "Learning to Adapt to Evolving Domains" (NeurIPS 2020)

Prerequisites

  • PyTorch >= 0.4.0 (with suitable CUDA and CuDNN version)
  • torchvision >= 0.2.1
  • Python3
  • Numpy
  • argparse
  • PIL

Dataset

Rotated MNIST: https://drive.google.com/file/d/1eaw42sg4Cgm34790AW_SKGCSkFosugl2/view?usp=sharing

Training

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

Acknowledgement

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/.

Citation

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}
}


Contact

If you have any problem about our code, feel free to contact

About

Code Release for Learning to Adapt to Evolving Domains

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages