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pred_s, f_s = model(x_s, src_idx * torch.ones(x_s.shape[0], dtype=torch.long).cuda(args.gpu), with_ft=True)
pred_t, f_t = model(x_t, trg_idx * torch.ones(x_t.shape[0], dtype=torch.long).cuda(args.gpu), with_ft=True)
I want to know what src_idx * torch.ones(x_s.shape[0], dtype=torch.long) and trg_idx * torch.ones(x_t.shape[0], dtype=torch.long) for

Hi, thanks for your sharing!
I attemp to add DSBN to CDAN, and the loss functions are the source domain cross-entropy loss and the conditional adversarial loss. I used the settings same as CDAN's except for the BN layer. However, the performance of 'CDAN+DSBN' even worse than 'CDAN + BN'.
I don't know why there is such a result. Did you ever conduct the experiments about 'CDAN+DSBN'？

can we provide the code of t-sne figure in your paper?

Hi,
After reading the paper, I am still puzzled about the difference between DSBN and AdaBN. I think the main difference between DSBN and AdaBN is an additional pseudo label loss in DSBN. However, I can't find the experiment results comparing DSBN and AdaBN in the paper. Have you reproduced AdaBN in your experiments?
Thanks.

Thanks for your inspiring work! I wonder why I cannot reproduce the result of Office31(A->W) in stage1 without DSBN. In the paper it's 91.3 while my best acc is 88.68.
This is my command:
python trainval_multi.py --model-name resnet50 --exp-setting office --sm-loss --adv-loss --source-datasets amazon --target-datasets webcam --batch-size 40 --save-dir output/resnet50_office_stage1 --print-console
Is there any mistake? Looking forward to you!

Hi, thank you for your excellent work.
I'm wondering if you can share the run script or train config in other experiments' settings, like Office-Home, Office-31, and domain-net. Thx a lot!

Very nice work and thank you for sharing, have you tried to use DSBN for regression tasks like detection?

Hi~ Thank you for your excellent work.
I just reproduced the experiment for VisDA dataset with MSTN by running the script in Readme. But I got 76.14% mean Acc and 91.91% 84.46% 75.78% 60.04% 94.91% 30.75% 89.29% 81.58% 90.06% 78.47% 84.28% 52.20% Class Acc which are relatively lower than that reported in your paper but similar with the result reported for CPUA. I run the code in a V100 without changing any hyper-parameter. I am wondering if there is something I missed in stage2 training. An early response would be appreciated.

Hello~ Thank you for your excellent work. I have a question.
In the result of multiple source domain (merged and separate) in your paper, what is the difference between BN and DSBN when the Domain is separated, do you design the same number of branch for BN , how to do BN in the separate case?

Hi,
thanks for the interesting work. I'm wondering how this works if there are images from different domains in one batch. Will different BN be used for each image individually or?
Thanks a lot?