Comments (1)
Thanks for your interest in our models! Our main results are reported on ResNet-152, so we've only released ResNet-152 models. Fow now we don't have plan to release the smaller models.
from imagenet-adversarial-training.
Related Issues (20)
- Question on running the evaluation HOT 11
- Format for input data HOT 5
- Image Preprocessing for ImageNet Validation HOT 1
- Does robustness gain originate from better converge of adv training? HOT 3
- Where are the pretrained models HOT 1
- resnet_model.py --> non_local_op HOT 3
- How can I transform the code of de-noising module into Caffe code HOT 1
- I get very high top 1 error rate 80.918% on clean Imagenet 2012 Val using R152.npz HOT 5
- Question about the code for converting variables to FP16 HOT 1
- Request of feature denoising resnet-152 checkpoint trained on clean images. HOT 1
- Not so sure of the adversarial training procedure HOT 1
- ResNet-101 baseline HOT 1
- how the 1*1 conv. judge whether removing noise or retaining signal?
- how the 1*1 conv. judge whether removing noise or retaining signal?
- low accuarcy of black box defending ImageNet2012 val dataset (with no noise) HOT 4
- Guide to use different denoising operation
- Want to Convert to Pytorch HOT 15
- Running without horovod? HOT 1
- How do you visualize features in the paper HOT 5
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