Awesome
Open-sampling
This repository is the official implementation of Open-sampling (ICML 2022: Open-Sampling: Exploring Out-of-Distribution Data for Re-balancing Long-tailed Datasets).
Requirements
To install requirements:
pip install -r requirements.txt
Training
To train the model(s) in the paper, run this command:
python main.py --dataset cifar10 --gpu 0 --imb_type exp --imb_factor 0.01 --alg open -p 100 --lambda_o 1 -ab 512
Evaluation
We also provide a pre-trained ResNet-32 model in ./checkpoint/cifar10_open_CE_exp_0.01_1_1/ckpt.best.pth.tar, and the training log is in ./log/cifar10_open_CE_exp_0.01_1_1/
To evaluate the pre-trained model on CIFAR-10, run:
python test.py --dataset cifar10 --gpu 0 --resume ./checkpoint/cifar10_open_CE_exp_0.01_1_1/ckpt.best.pth.tar
What's More?
Below are my other research works related to this topic:
- Using OOD examples to improve robustness against inherent noisy labels: NeurIPS 2021 | Code
- Improving OOD detection by logit normalization: ICML 22 | Code
Citation
If you find this useful in your research, please consider citing:
@inproceedings{wei@open,
title={Open-Sampling: Exploring out-of-distribution data for re-balancing long-tailed datasets},
author={Wei, Hongxin and Tao, Lue and Xie, Renchunzi and Feng, Lei and An, Bo},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2022},
organization={PMLR}
}