lijichang / lnl-nce Goto Github PK

Hi,
It would be great if you can tell me why loss of unlabeled data should be calculated with 'cat[inputs_us, inputs_xs]' in cifar100,but not in other dataset such as cifar10.I didn't find the explanation for that in paper.
Thanks!
The part of the code is:

    # label consistency regularization for unlabeled data
    if (args.dataset == 'cifar100') and ((args.r==0.2) or (args.r==0.5)):
        all_inputs, all_labels, all_masks = torch.cat([inputs_us, inputs_xs], dim=0), torch.cat([labels_u,  labels_x], dim=0), torch.cat([mask_u, mask_x], dim=0)
        all_logits = net(all_inputs)
        Lu = (F.cross_entropy(all_logits, all_labels, reduction='none') * all_masks.float()).mean()
    else:
        logits_us = net(inputs_us)
        Lu = (F.cross_entropy(logits_us, labels_u, reduction='none') * mask_u.float()).mean()

Your work is really interesting and impressive!

And I wonder when the code will be released?

Hi. First of all, I really want to say I am really inspired by your awesome work!

However, while reproducing the results from the paper I've encountered an issue.

With all the hyperparameters set same as the paper I can't reproduce the same result with 20% symmetric noise.

The paper says the best results is 96.2±0.09, but what I get consistently is around 95.2.

Is there something I missed? I would really appreciate your response, thanks!

I would like to know how to run LNL-NCE on custom datasets, the repository code seems very specific to datasets, have you made LNL-NCE into a tool?

Thanks.