Hi, thanks for your excellent work!
I have problems during the reproduction. From your paper, the accuracy of Ar->Cl is 57.1 in office-home, however, I only got 7% which is extremely low. This situation also exists in other domain transfer experiment, such as Ar->Pr, Ar->Re, which I only get 26%, 27%,respectively. It also exists in the office31 dataset.
My command is typed as your github says, python image_target.py --cls_par 0.3 --da uda --output_src ckps/source/ --output ckps/target/ --gpu_id 0 --dset office --s 0
I didn't change any code and commands. I am confused about this thing for a long time , looking forward to your reply! Thanks very very much. Here are Ar->Cl, Ar->Re snapshot respectively.
I also didn't change the command to train source model. And the accuracy of model in Ar is still high, which is shown below:
I am confused about this thing for a long time , looking forward to your reply! Thanks very very much.
Hi, thanks for your excellent work! I have problems during the reproduction. From your paper, the accuracy of Ar->Cl is 57.1 in office-home, however, I only got 7% which is extremely low. This situation also exists in other domain transfer experiment, such as Ar->Pr, Ar->Re, which I only get 26%, 27%,respectively. It also exists in the office31 dataset. My command is typed as your github says,
python image_target.py --cls_par 0.3 --da uda --output_src ckps/source/ --output ckps/target/ --gpu_id 0 --dset office --s 0I didn't change any code and commands. I am confused about this thing for a long time , looking forward to your reply! Thanks very very much. Here are Ar->Cl, Ar->Re snapshot respectively.I also didn't change the command to train source model. And the accuracy of model in Ar is still high, which is shown below: