There is a bit of inconsistency between the code and the paper

[ALEX_ZGH]

Hi,I find there is a bit of inconsistency between the code and the paper.
In codes,adversarial loss is caculated by
loss_adv_tgt = 0.001*soft_label_cross_entropy(tgt_D_pred, torch.cat((tgt_soft_label, torch.zeros_like(tgt_soft_label)), dim=1))
in which you caculate the loss between target prediction and target labels.

However,in the paper,the adversarial loss is written like this:

Adverasarial loss is caculated between target label and source prediction.
right?

[Haoran Wang]

Hi, thanks for your question.
Actually, as you can see in the code, model_D has a 2K channels' output. So the tgt_D_pred includes P(d=0, c=k|f_j) and P(d=1, c=k|f_j) for all class k in the formula. We set a_{jk} for all P(d=1, c=k|f_j) as 0, which makes our implementation consistent with the formula in the paper.