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View Code? Open in Web Editor NEWThe Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
pseudo_labeled = outputs.max(1)[1] #outputs should be unlabel data,but your code outputs is model(label_data)??????
在linux按照指令操作为什么还是不行?
'af' = 3 in the paper Pseudo-Label The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks ,
I would like to know whether 0.3 in here help model perform better than 3 in paper.
`# make the pseudo label for unlabeled data
output2 = net(img2);
_, label2 = torch.max(output2, 1);
#semi-supervised loss
loss = criterian(output1, label1) + alpha * criterian(output2, label2);`
Could you please explain why criterian() for unlabeled data works? Because label2 is actually calculated from output2, they are the same indeed? Why non-zero loss is obtained?
paper shows that they pretraining use all data in a unsupervised way.I'm wondering where you pretrain your net in your codes.
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