CVPR · 2023 · Conference paper · Top venue

EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-Distilled Regularization

Junha Song, Jungsoo Lee, In So Kweon, Sungha Choi

Korea Advanced Institute of Science and Technology · Qualcomm (United Kingdom) · Kootenay Association for Science & Technology

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Published in
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Date
2023-06-01
Citations
175
arXiv
2303.01904
Cite
@inproceedings{song2023ecotta,
  title = {EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-Distilled Regularization},
  author = {Junha Song and Jungsoo Lee and In So Kweon and Sungha Choi},
  year = {2023},
  booktitle = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  eprint = {2303.01904},
  archivePrefix = {arXiv},
  doi = {10.1109/CVPR52729.2023.01147},
  url = {https://arxiv.org/abs/2303.01904},
}