evgeneus / graph-domain-adaptaion Goto Github PK

Hi,
Thank you for the awesome work! Can you provide the source pretrained model or any command to reproduce your result in Table 4 row 1 (w/o target source train) result?

I am a student who are not able to afford an expensive graphics card. I have trained the data in Sagemaker Studio Lab, but the website provide little GPU time so that I can't finish the 3rd step. It will be appeciated that you can send the trained model to me via email [email protected]. Thank you.

From my understanding this code by default runs the D-CGCT variant of your method. However I am interested in comparing its performances with a MTDA method I am working on, which does not use domain labels for target samples. What should I do to run D-CGCT? Should I simply put all target data in a single txt file and use it as if it was a single target?

Thanks in advance

Hi, thanks for your inspiring work! I have read the code and have some doubt about it. During the curriculum learning, why not remove the pseudo labelled target samples from the target domain after they are added to the source domain?
Expect your reply. Thanks.

Wanted to know that whether the accuracy reported in the paper is achieved through test split or train split.

Thanks.

Thank you for the fantastic work! I realized that the "ndomain" parameter in config is always set to 2, which should be the target-combined setting in your paper.

May I know how did you train the other methods in the multi-target setting? For example, in the case of CDAN with k target domains, did you simply set the output dimension of domain classifier to be k+1?