NaR

Code for reproducing results in AAAI-2020 submission.

Dependencies

• pytorch-v1.0+
• numpy 1.16.3
• progress v1.5

To run the code

python cifar_nar.py --arch resnet --arch2 resnet --depth 32 --depth2 32 --epochs 300 --schedule 150 225 --gamma 0.1 --wd 1e-4 --lam 0.5 --alpha 0.1 --dataset cifar100 --memo test --gpu-id 0 --manualSeed 201905

You can try other types networks.

Configuration table:

Model	Depth	weight decay	gamma	epochs	schedule
plaincnn	6	5e-4	0.1	200	60 120 160
resnet	32	1e-4	0.1	300	150 225
preresnet	110	1e-4	0.1	300	150 225
wrn	28	5e-4	0.2	200	60 120 160

Robustness test on models trained with NaR

Here, we compare robustness of the model trained with NaR and vanilla training strategy. The robust test consists of two parts, one is input noise test and the other is parameter noise test. The noise type is supposed to be Gaussian noise with std in [0.01, 0.05, 0.1, 0.15, 0.2, 0.3]. Note that we only test the last linear layer of the model's parameter.

For each noise level, we've run 10 times and report the average performance. For input noise: For parameter noise: