This is a reproducing code for Dynamic Importance Weighting (DIW) in the NeurIPS'20 paper: Rethinking Importance Weighting for Deep Learning under Distribution Shift.
Link to the paper: https://proceedings.neurips.cc//paper/2020/file/8b9e7ab295e87570551db122a04c6f7c-Paper.pdf
The code was developed and tested based on the following environment.
- python 3.8
- pytorch 1.6.0
- torchvision 0.7.0
- cudatoolkit 10.2
- cvxopt 1.2.0
- matplotlib
- sklearn
- tqdm
You can run an example code of DIW on Fashion-MNIST under 0.4 symmetric label noise.
python diw.py
After running python diw.py, a output figure and text file of test accurary are made in ./output/ by default.
If the code is useful in your research, please cite the following:
Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama. Rethinking Importance Weighting for Deep Learning under Distribution Shift. NeurIPS 2020.