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View Code? Open in Web Editor NEWA pytorch implementation for "Neighborhood Collective Estimation for Noisy Label Identification and Correction", which is accepted by ECCV2022.
A pytorch implementation for "Neighborhood Collective Estimation for Noisy Label Identification and Correction", which is accepted by ECCV2022.
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.
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