shelhamer / tent

ICLR21 Tent: Fully Test-Time Adaptation by Entropy Minimization

⛺️ Tent: Fully Test-Time Adaptation by Entropy Minimization

This is the official project repository for Tent: Fully-Test Time Adaptation by Entropy Minimization by Dequan Wang*, Evan Shelhamer*, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell (ICLR 2021, spotlight).

⛺️ Tent equips a model to adapt itself to new and different data during testing ☀️ 🌧 ❄️. Tented models adapt online and batch-by-batch to reduce error on dataset shifts like corruptions, simulation-to-real discrepancies, and other differences between training and testing data. This kind of adaptation is effective and efficient: tent makes just one update per batch to not interrupt inference.

To illustrate the tent method and fully test-time adaptation setting we provide example code for adapting to image corruptions on CIFAR-10-C. The purpose of the example is explanation, not reproduction: exact details of the model architecture, optimization settings, etc. may differ from the paper. That said, the results should be representative, so do give it a try and experiment!

Please check back soon for reference code to exactly reproduce the ImageNet-C results in the paper.

Example: Adapting to Image Corruptions on CIFAR-10-C

This example compares a baseline without adaptation (source), test-time normalization for updating feature statistics during testing (norm), and our method for entropy minimization during testing (tent).

• Dataset: CIFAR-10-C, with 15 corruption types and 5 levels.
• Model: WRN-28-10, the default model for RobustBench.

Usage:

python cifar10c.py --cfg cfgs/source.yaml
python cifar10c.py --cfg cfgs/norm.yaml
python cifar10c.py --cfg cfgs/tent.yaml

Result: tent reduces the error (%) across corruption types at the most severe level of corruption (level 5).

	mean	gauss_noise	shot_noise	impulse_noise	defocus_blur	glass_blur	motion_blur	zoom_blur	snow	frost	fog	brightness	contrast	elastic_trans	pixelate	jpeg
source code config	43.5	72.3	65.7	72.9	46.9	54.3	34.8	42.0	25.1	41.3	26.0	9.3	46.7	26.6	58.5	30.3
norm code config	20.4	28.1	26.1	36.3	12.8	35.3	14.2	12.1	17.3	17.4	15.3	8.4	12.6	23.8	19.7	27.3
tent code config	18.6	24.8	23.5	33.0	12.0	31.8	13.7	10.8	15.9	16.2	13.7	7.9	12.1	22.0	17.3	24.2

See the full results for this example in the wandb report.

Correspondence

Please contact Dequan Wang and Evan Shelhamer at dqwang AT cs.berkeley.edu and shelhamer AT google.com.

Citation

If the tent method or fully test-time adaptation setting are helpful in your research, please consider citing our paper:

@inproceedings{wang2021tent,
  title={Tent: Fully Test-Time Adaptation by Entropy Minimization},
  author={Wang, Dequan and Shelhamer, Evan and Liu, Shaoteng and Olshausen, Bruno and Darrell, Trevor},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=uXl3bZLkr3c}
}