tanlingp
tanlingp
#I am trying to use the CIFAR10 dataset as the source domain to train the generator, but it has been unable to converge. If you can, please provide the code....
the learnning rate set 0.00001，batch size set 128 | | tlp6378 | | ***@***.*** | ---- Replied Message ---- | From | Qilong ***@***.***> | | Date | 05/17/2023 10:00...
Thank you very much for your help, it is indeed the reason for the wrong version
The version given by the author
For the Robustness on defensive approaches section, run the original code. not quite sure how to run it
At the risk of asking, why did I find a precision of 0.0% for both clean and adversarial samples in my test?
Have you used imagenet-compatible generated adversarial samples for testing? It's incredible that his clean samples also have an accuracy of 0. Looking forward to your reply
The above problems have been solved and are mainly a matter of image sequencing. Thank you very much for your patience in answering. Thank you so much.
This inception model is 6% less attackable than your paper for resnet50 and vgg19
The experimental setup is all according to the code you provided. Normally the inception model input seems to be 229✖229, here it is 224✖224, what kind of setup do you...