zijian-hu / simple Goto Github PK

Hello, Zijian Hu

I have a question because the mini-imagenet file uploaded to github is no longer downloaded.

I would be really grateful if you could tell me how you configured train/test of 50000/10000 about mini-imagenet dataset.

If there is a file, I would be very grateful if you could give me a link.

I look forward to your kind response. Thank you once again for your valuable time.

Hi, thanks for your work!

In your paper, Tab.3, Res18 can achieve 49.39 on miniImagenet.

Here is my wandb log (the code is your repo):

The bottom one is the baseline by disabling PairLoss. Top two are for SimPLE on two different machines (same config). It can be seen that the accuracy curve is almost flat. I think it is hard to get additional 5+% in the following training steps. And I checked your code. You don't use any lr scheduler. So I think the acc. may not increase sharply in the following step.

Could you please share a config or a wandb log of resnet18-miniimagenet?

Best wishes.

Thanks a lot for your released codes and your excellent work. I have some questions about the reported results in your paper.
(1) Does all reported results in the paper obtained by the final EMA model instead of the trainable student model?
(2) Does the reported results are obtained by calculating the mean accuracy value of the latest several (e.g., 20) iterations/epochs during the training phase or just select the best accuracy of someone iteration/epoch, or any other protocols?

Dear Zijian,

Thank you for your contribution! I got a question when reading the pair loss computing part.

I got confused about whether the similarity should be computed between pseudo labels generated from weak augmented data and prediction of strong augmented data, or, pseudo labels generated from weak augmented data among different input pictures.

If the latter one is right, does line 70 means that: if a weak-augmented-image(A)-generated pseudo label with high confidence is similar enough to the pseudo label from another input image B, then the distance between pseudo label A and prediction of strong augmented image B should be minimized?

distance_ij = self.get_distance_loss(loss_input, targets_i, dim=1, reduction='none')

Thank you for your time. I would appreciate it if I would be replied.

Best,
Vivian

Hi, thanks for your excellent work.

When will you release your code?

Thanks

Dear zijianhu

Hello, I can't download the domainnet_real dataset you used, can you fix it to the latest version?