Comments (1)
It may be noise; I should say we did not run with the exact code used in this repository, but it should be representative enough. I expect that the perturbation stability and calibration is quite improved by the JSD loss. If you found it didn't help with corrupution robustness, perturbation stability, or calibration on CIFAR-10 and CIFAR-100, then there is a bug. If you suspect a bug let us know.
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Related Issues (20)
- Reproduce of Baseline
- A question in "third_party/WideResNet_pytorch/wideresnet.py out = self.relu2(self.bn2(self.conv1(out))) HOT 2
- `depth` is constant each `width` HOT 2
- There is a big gap between the results of the code and the results in the paper on CIFAR10-C HOT 2
- What about the performance on regular classification problems? HOT 5
- KL divergenve HOT 1
- Issue loading pretrained weights HOT 3
- mean and std for every channel is 0.5 instead of (0.5071, 0.4867. 0.4408) as mean and (0.2657, 0.2565, 0.2761) as std HOT 2
- Jensen–Shannon divergence HOT 2
- Corruption acc. of a Resnet50 trained on cifar10 with augmix HOT 2
- augmentations used in augmix HOT 3
- command for imagenet evaluation
- Use combined with Object Detection
- none
- The CIFAR-10-C dataset are not normalized HOT 1
- CIFAR-10/ImageNet-P code HOT 2
- Can you share any acc information per epoch? HOT 3
- Testing with the best model HOT 4
- Repeated Evaluation in ImageNet HOT 1
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