Comments (1)
Hadisalman
commented on June 2, 2024
@joneswong Apologies for the late reply. Yea we don't use random start in any of our experiments, and yup we agree you might be able to get even better results by doing random start. Though in the presence of noise_sd, I don't think it would change things since there is an inherent randomness already in the smoothed classifier when it is evaluated multiple at the same data point. Hope this helps!
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Related Issues (10)
- InputCenterLayer？ HOT 1
- unexpected results from pretrained models (CIFAR-10) HOT 2
- Issue with replication yml/training file HOT 3
- Model predictions incorrect -> possible dataloader issue? HOT 7
- Download the archive of pretrained model failed HOT 1
- Shuffling in ImageNet dataloader HOT 1
- Cannot download your trained models HOT 3
- Do we need to clip our new input into [0,1] after adding noise ? HOT 2
- train_pgd.py noise parameter HOT 1
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