Would you please provide the commands used to reproduce the results on VisDA-C dataset? Your paper stated that the result for SHOT-IM++ and SHOT++ is 85.0 and 87.3 respectively, but I could only get 82.8~ by running python image_source.py --output ckps/source/ --da uda --gpu_id 1 --dset VISDA-C --net resnet101 --lr 1e-3 --max_epoch 10 --s 0 and python image_target.py --cls_par 0.3 --da uda --dset VISDA-C --gpu_id 0 --s 0 --output_src ckps/source/ --output ckps/target/ --net resnet101 --lr 1e-3 for training. Thanks.

First of all, thank you for your outstanding work. I have a question about the performance of Source-only ++(SHOT++), which is superior than Souce-only (SHOT) with a large margin. But I didn't see the difference with SHOT in the image_source script. Is this performance evaluted after executing "python image_mixmatch.py --ps 0.0 --cls_par 0.0 --model source --gpu_id $1 --s $s --output_tar "ckps/s"$2 --output "ckps/mm"$2 --seed $2 --dset office --max_epoch 100"? Is this trained using target data?

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.