GitHub repo for IJCNN 2023 paper
- models: contains saved model checkpoints
- results: contains .pkl files generated from testing
- CIFAR-10-C: .npy files for each corruption
- CIFAR-100-C: .npy files for each corruption
- Tiny-ImageNet-C: folders for each corruption
- 'brightness'
- 1: severity 1
- class1
To train CIFAR-10 ResNet-18 model using Standard training, batch size 512:
python train.py cifar10 resnet18 512 standard
To train CIFAR-10 ResNet-18 model using DiGN training, batch size 512:
python train.py cifar10 resnet18 512 dign_gn
Other baselines are available in train.py
, including:
- AT
- TRADES
- RSE
- DeepAugment
- AugMix
- AugMax
- DiGN w.o. CR
To evaluate robustness under corruptions for a CIFAR-10 ResNet-18 model:
python test-rob-c.py cifar10 resnet18 512 [Boolean_test_time_ensemble] [model_name] [Boolean_noise_only_eval]
To evaluate a model only on digital noise corruptions (mCA-N), assuming the model in models
directory saved as 'RN18_cifar10_DIGN_model_X', use:
python test-rob-c.py cifar10 resnet18 512 False DIGN_model_X True
and for evaluating on all common corruptions (mCA), use instead:
python test-rob-c.py cifar10 resnet18 512 False DIGN_model_X False
To evaluate uncertainty calibration under corruptions for a CIFAR-10 ResNet-18 model:
python test-cal-c.py cifar10 resnet18 512 [Boolean_test_time_ensemble] [model_name] [Boolean_noise_only_eval]
To evaluate a model only on digital noise corruptions (RMSE-N), assuming the model in models
directory saved as 'RN18_cifar10_DIGN_model_X', use:
python test-cal-c.py cifar10 resnet18 512 False DIGN_model_X True
and for evaluating on all common corruptions (RMSE), use instead:
python test-cal-c.py cifar10 resnet18 512 False DIGN_model_X False
For citing this paper or code, please use the following:
@inproceedings{DiGN,
title={Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration},
author={Theodoros Tsiligkaridis and Athanasios Tsiligkaridis},
booktitle={Proceedings of the International Joint Conference on Neural Networks, IJCNN},
month={June},
year={2023}
}