Questions about self-supervised learning on cifar10

Thanks for sharing the codes! This work is really interesting to me. My questions are as follows:

I’m trying to reproduce the results in Table 2. Specifically, I trained the models with/without self-supervised pre-training (SSP). However, the baselines (w.o. SSP) consistently outperform those with SSP under different training rules (including None, Resample, and Reweight). The best precisions are presented below. For each experimental setting, I run twice to see if the results are stable, so there’re two numbers per cell.

For your reference, I used the following commands:

• Train Rotation

python pretrain_rot.py --dataset cifar10  --imb_factor 0.01 --arch resnet32

• Train baseline

python train.py --dataset cifar10 --imb_factor 0.01 --arch resnet32 --train_rule None 

• Train baseline + SSP

python train.py --dataset cifar10 --imb_factor 0.01 --arch resnet32 --train_rule None --pretrained_model xxx 

[YyzHarry]

the batch size is set to 256 while the default batch size is 128 in DRW source code

That’s a good catch. I just checked the setting when I ran the experiments, and the batch size I used is 128 on CIFAR. Thought there might be some inconsistency on the default value when I clean up the code (already updated it).

Regarding your questions, I just quickly ran three experiments. For baseline on CIFAR-10-LT with None, I got 71.03%. By using None + SSP, I got 73.99%. For DRW + SSP, I got 77.45%, which is even slightly higher than the number reported in our paper. I’m using the Rotation checkpoint I provided in this repo, which has 83.07% test acuracy, similar to yours. I also checked your log, which seems fine to me. So currently I’m not sure what causes the difference. I would suggest you try using the Rotation SSP checkpoint I provided, to see if there’s any difference.

Otherwise, you may want to check whether the pre-trained weights are loaded correctly, or the exact training setting such as PyTorch version (1.4 for this repo), or how many GPUs used (for CIFAR experiments only 1), etc.

[87nohigher]

I met the same problem. Did you solve the question? How did you solve it? Thanks

Thanks for the help! Let me try these ideas.