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

  • Published:2023-06 Issue: Volume: Page:
  • ISSN:
  • Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • language:
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Author:

Song Junha1,Lee Jungsoo1,Kweon In So2,Choi Sungha1

Affiliation:

1. Qualcomm AI Research

2. KAIST

Publisher

IEEE

Reference64 articles.

1. A Survey of On-Device Machine Learning

2. Rep-net: Efficient on-device learning via feature reprogramming;yang;CVPR,0

3. Transadapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation

4. DA3: Dynamic Additive Attention Adaption for Memory-Efficient On-Device Multi-Domain Learning

5. Test-time training with masked autoencoders;gandelsman;NeurIPS,0

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1. Advancing Model Generalization in Continuous Cyclic Test-Time Adaptation with Matrix Perturbation Noise;Mathematics;2024-09-10

2. Class-incremental learning with causal relational replay;Expert Systems with Applications;2024-09

3. A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts;International Journal of Computer Vision;2024-07-18

4. Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
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