Hi,
In the paper "Transferable Representation Learning with Deep Adaptation Networks", you use cross-entropy loss (which is corresponding to equation 8 in the paper) to minimize the uncertainty of predicting the labels of the target data.
I find the corresponding implementation of that equation which is defined as EntropyLoss() in loss.py. In the paper, the total loss is composed of three main parts: the classification loss, the mmd loss and the cross-entropy loss.
What confused me is that in train.py, you do add the mmd loss and the classification loss together, but you don't actually add the cross-entropy loss. I am wondering do I miss something or do you do it on purpose?
Looking forward to hearing from you soon.
Thank you,
Ke
Hi,
In the paper "Transferable Representation Learning with Deep Adaptation Networks", you use cross-entropy loss (which is corresponding to equation 8 in the paper) to minimize the uncertainty of predicting the labels of the target data.
I find the corresponding implementation of that equation which is defined as EntropyLoss() in loss.py. In the paper, the total loss is composed of three main parts: the classification loss, the mmd loss and the cross-entropy loss.
What confused me is that in train.py, you do add the mmd loss and the classification loss together, but you don't actually add the cross-entropy loss. I am wondering do I miss something or do you do it on purpose?
Looking forward to hearing from you soon.
Thank you,
Ke