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A Probabilistic Framework for Lifelong Test-Time Adaptation

Test-time adaptation (TTA) is the problem of updating a pre-training source model at inference time given test input(s) from a different target domain. Most existing TTA approaches assume the setting in which the target domain is stationary, i.e., all the test inputs come from a single target domain. However, in many practical settings, the target domain distribution might exhibit a lifelong/continual shift over time. Moreover, existing TTA approaches also lack the ability to provide reliable uncertainty estimates, which is crucial when distribution shifts occur between the source and target domain. To address these issues, we present PETAL (Probabilistic lifElong Test-time Adaptation with seLf-training prior), which solves lifelong TTA using a probabilistic approach, and naturally results in (1) a student-teacher framework, where the teacher model is an exponential moving average of the student model, and (2) regularizing the model updates at inference time using the source model as a regularizer. To prevent model drift in the lifelong/continual TTA setting, we also propose a data-driven parameter restoration technique that contributes to reducing the error accumulation and maintaining the knowledge of recent domains by restoring only the irrelevant parameters. In terms of predictive error rate as well as uncertainty based metrics such as Brier score and negative log-likelihood, our method achieves better results than the current state-of-the-art for online lifelong test-time adaptation across various benchmarks, such as CIFAR-10C, CIFAR-100C, ImageNetC, and ImageNet3DCC datasets.

Setting up the environment

Create the following conda envrionment:

conda env create -f environment.yml

Then activate the environment:

conda activate petal

CIFAR10/100

For CIFAR10-to-CIFAR10C/CIFAR100-to-CIFAR100C experiments, first change the directory using:

cd cifar

CIFAR10-to-CIFAR10C Experiments

  1. Download the pre-trained WideResNet-28 from RobustBench from the link (url is obtained from robustbench/model_zoo/cifar10.py): https://drive.google.com/file/d/1t98aEuzeTL8P7Kpd5DIrCoCL21BNZUhC/view
  2. Rename the downloaded file "natural.pt.tar" to "Standard.pt" and move inside the directory ckpt/cifar10/corruptions/
  3. Run python train-swag-diagonal-cifar10.py for further training on source domain training data. This will create the files Standard_cov.pt and Standard_swa.pt inside the directory ckpt/cifar10/corruptions/
  4. Now, run the CIFAR10-to-CIFAR10C adaptation experiment for PETAL (FIM) by running:
bash run_cifar10_petalfim.sh
  1. Run CIFAR10-to-CIFAR10C adaptation experiment for PETAL (SRes) by running:
bash run_cifar10_petalsres.sh

CIFAR100-to-CIFAR100C Experiments

  1. Run "python train-swag-diagonal-cifar100.py" for further training on source domain training data. This will create the files Hendrycks2020AugMix_ResNeXt_cov.pt and Hendrycks2020AugMix_ResNeXt_swa.pt inside the directory ckpt/cifar100/corruptions/
  2. Now, run the CIFAR100-to-CIFAR100C adaptation experiment for PETAL (FIM) by running:
bash run_cifar100_petalfim.sh
  1. Run CIFAR100-to-CIFAR100C adaptation experiment for PETAL (SRes) by running:
bash run_cifar100_petalsres.sh

CIFAR10-to-CIFAR10C gradual corruption severity level change experiments

  1. Download the pre-trained WideResNet-28 from RobustBench from the link (url is obtained from robustbench/model_zoo/cifar10.py): https://drive.google.com/file/d/1t98aEuzeTL8P7Kpd5DIrCoCL21BNZUhC/view
  2. Rename the downloaded file natural.pt.tar to Standard.pt and move inside the directory ckpt/cifar10/corruptions/
  3. Run python train-swag-diagonal-cifar10.py for further training on source domain training data. This will create the files Standard_cov.pt and Standard_swa.pt inside the directory ckpt/cifar10/corruptions/
  4. Now, run CIFAR10-to-CIFAR10C gradual adaptation experiment for PETAL (FIM) by running:
bash run_cifar10_gradual.sh

ImageNet

For ImageNet-to-ImageNetC experiments, first change the directory using:

cd imagenet

ImageNet-to-ImageNetC Experiments

  1. Download the ImageNet train dataset from: link. Upon extracting ILSVRC2012_img_train.tar, the ILSVRC2012_img_train directory will be extracted. Move ILSVRC2012_img_train directory inside the data/ directory.
  2. Run python train-swag-diagonal-imagenet.py for further training on source domain training data. This will create the files Standard_R50_cov.pt and Standard_R50_swa.pt inside the directory ckpt/cifar10/corruptions/
  3. Download ImageNet-C from: link. Keep the files inside the directory data/ImageNet-C/
  4. Now, run ImageNet-to-ImageNetC adaptation experiment for PETAL (FIM) by running:
bash run_petalfim.sh
  1. Run the ImageNet-to-ImageNetC adaptation experiment for PETAL (SRes) by running:
bash run_petalsres.sh
  1. Run python eval_corruptionwise.py to get final averaged results.

ImageNet

For ImageNet-to-ImageNet3DCC experiments, first change the directory using:

cd imagenet

ImageNet-to-ImageNet3DCC Experiments

  1. Download the ImageNet train dataset from: link. Upon extracting ILSVRC2012_img_train.tar, the ILSVRC2012_img_train directory will be extracted. Move ILSVRC2012_img_train directory inside the data/ directory.
  2. Run python train-swag-diagonal-imagenet.py for further training on source domain training data. This will create the files Standard_R50_cov.pt and Standard_R50_swa.pt inside the directory ckpt/cifar10/corruptions/
  3. Download ImageNet-3DCC by running: download_imagenet3d.sh located inside the directory data/ImageNet-3D/. Ensure that the files are stored inside the directory data/ImageNet-3DCC/
  4. Now, run ImageNet-to-ImageNet3DCC adaptation experiment for PETAL (FIM) by running:
bash run_petalfim_imagenet3dcc.sh
  1. Run python eval_corruptionwise_img3d.py to get final averaged results.

This codebase uses the following repositories:

  • CoTTA link
  • TENT link
  • KATANA for augmentation. link
  • Robustbench link
  • 3DCommonCorruptions link for download_imagenet3d.sh

Citation

Please use the following to cite our work.

@inproceedings{brahma2023probabilistic,
  title={A Probabilistic Framework for Lifelong Test-Time Adaptation},
  author={Brahma, Dhanajit and Rai, Piyush},
  booktitle={Proceedings of Conference on Computer Vision and Pattern Recognition},
  year={2023}
}