Hello. Thank you for sharing your code. I noticed your comment. Is the arXiv paper the latest version? In your CVPR paper, the accuracy increased, but when I reproduced your code, the result was lower than the accuracy reported in the CVPR paper. whats the latest version ?
Apologies for the oversight in our paper regarding the incorrect upload of the results for CIFAR-10.
We have updated our GitHub repository and reported the final results for CIFAR-10.
Compared to the latest state-of-the-art work by BCL[1], our results are still 3% higher!
We will upload the latest paper on arXiv as soon as possible.
The experimental setup was as follows:
python main.py --dataset cifar10 -a resnet32 --num_classes 10 --imbanlance_rate 0.01 --beta 0.5 --lr 0.01 --epochs 200 -b 64 --momentum 0.9 --weight_decay 5e-3 --resample_weighting 0.0 --label_weighting 1.2 --contrast_weight 4.
Thank you very much for your question, which has helped us improve our work!
[1] Jianggang Zhu, ZhengWang, Jingjing Chen, Yi-Ping Phoebe Chen, and Yu-Gang Jiang. Balanced contrastive learning for long-tailed visual recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6908–6917, 2022. 2, 3, 5, 6
@scutfrank @ug-kim @CxC-ssjg
Hello. Thank you for sharing your code. I noticed your comment. Is the arXiv paper the latest version? In your CVPR paper, the accuracy increased, but when I reproduced your code, the result was lower than the accuracy reported in the CVPR paper. whats the latest version ?
We have updated our GitHub repository and reported the final results for CIFAR-10. Compared to the latest state-of-the-art work by BCL[1], our results are still 3% higher! We will upload the latest paper on arXiv as soon as possible.
The experimental setup was as follows: python main.py --dataset cifar10 -a resnet32 --num_classes 10 --imbanlance_rate 0.01 --beta 0.5 --lr 0.01 --epochs 200 -b 64 --momentum 0.9 --weight_decay 5e-3 --resample_weighting 0.0 --label_weighting 1.2 --contrast_weight 4.
Thank you very much for your question, which has helped us improve our work!
[1] Jianggang Zhu, ZhengWang, Jingjing Chen, Yi-Ping Phoebe Chen, and Yu-Gang Jiang. Balanced contrastive learning for long-tailed visual recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6908–6917, 2022. 2, 3, 5, 6 @scutfrank @ug-kim @CxC-ssjg
Originally posted by @ynu-yangpeng in https://github.com/ynu-yangpeng/GLMC/issues/3#issuecomment-1547420066