I train ResNet18 in Cifar100

[dreamer121121]

I train ResNet18 in Cifar100 with augmax and without augmax but i find that i can got 77.5% accuracy without augmax ,while i can only got 76.3% with augmax, can u give some explainations?

[htwang14]

Hi! What Lambda and steps values did you use? In the paper, we mentioned that on CIFAR100 with ResNet18 we use Lambda=1.0. steps should always be 10 on CIFAR10/100 and Tiny ImageNet. I've updated README in 8ca00c0 to reflect these details. Sorry for the confusion.

[dreamer121121]

Hi! What Lambda and steps values did you use? In the paper, we mentioned that on CIFAR100 with ResNet18 we use Lambda=1.0. steps should always be 10 on CIFAR10/100 and Tiny ImageNet. I've updated README in 8ca00c0 to reflect these details. Sorry for the confusion.
Thanks for ur reply, i set the lambda=1.0 then i got ur results in CIfar100. but, i have another question, how to choose the lambda when using the augmax in different datasets?

[htwang14]

My suggestion is to first try Lambda~10. Usually AugMax gets similar performance with different Lambda values. The experiment on CIFAR100 with ResNet18 is the only exception I encountered, where smaller Lambda is preferred. My assumption is that ResNet18 has limited model capacity, so adding too much regularizations (by using a large Lambda) make the model over-smooth and hurts performance.

[dreamer121121]

My suggestion is to first try Lambda~10. Usually AugMax gets similar performance with different Lambda values. The experiment on CIFAR100 with ResNet18 is the only exception I encountered, where smaller Lambda is preferred. My assumption is that ResNet18 has limited model capacity, so adding too much regularizations (by using a large Lambda) make the model over-smooth and hurts performance.

I want to apply Augmax to semantic segmentation, can you give me some advise?

[htwang14]

I think AugMax is directly appliable on semantic segmentation tasks: just replace the classification loss with dense prediction loss (both the cross-entropy and the consistency loss should be replaced with a dense prediction version). As for benchmark datasets, you can use this one https://github.com/bethgelab/robust-detection-benchmark, which adds corruptions on segmentation/detection datasets. Although it's named as object detection, I think is can also be used for segmentation (at least Cityscapes and Pascal VOC have segmentation annotations).