Open tanlingp opened 1 year ago
Hi @tanlingp ,
It does take time. Slow running speed is probably a common problem of most diffusion models at present. For a quick look at the results, maybe you can just run it on the demo images we provide.
At the risk of asking, why did I find a precision of 0.0% for both clean and adversarial samples in my test?
Sometimes it may be because the given input image itself is a difficult sample for the classifier. In this case, both the original clean image and the attacked image will lead to 0.0% accuracy.
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.
Glad to see the problem solved 👍 .
Thanks for your excellent work. I found that it took six hours to train just 1,000 images. This is certainly cost intensive. I would like to ask if this is a personal factor for me or for that model, and also would like to ask if that brute requirement of 1000 images? Would it be a smaller amount of data? I look forward to your reply, thank you very much.