This is a question regarding the paper, not the code.
I tried to parse through your code, but as I am not familiar with TF, there are more confusions.
As far as I understand, MentorNet (DD) is only trained with a small clean set, right?
If the label spaces are matched between clean set and large noisy set, it is pretty clear.
My question is, as there are different number of classes between CIFAR10 and CIFAR100, how can you train label embedding layer in CIFAR10 and deploy in CIFAR100?
This is a question regarding the paper, not the code. I tried to parse through your code, but as I am not familiar with TF, there are more confusions.
As far as I understand, MentorNet (DD) is only trained with a small clean set, right? If the label spaces are matched between clean set and large noisy set, it is pretty clear.
My question is, as there are different number of classes between CIFAR10 and CIFAR100, how can you train label embedding layer in CIFAR10 and deploy in CIFAR100?
Thank you :D