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CTA: Cross-Task Alignment for Better Test Time Training

Created by
  • Haebom

Author

Samuel Barbeau, Pedram Fekri, David Osowiechi, Ali Bahri, Moslem Yazdanpanah, Masih Aminbeidokhti, Christian Desrosiers

Outline

In this paper, we propose a novel method, Cross-Task Alignment (CTA), to improve test-time training (TTT). Unlike existing TTT methods, CTA does not require a special model architecture, and aligns supervised learning encoders with self-supervised learning encoders, inspired by the success of multi-modal contrastive learning. This process strengthens the alignment between the learned representations of the two models, which mitigates the risk of gradient interference, preserves the intrinsic robustness of self-supervised learning, and enables more meaningful updates at test time. Experimental results show that it significantly improves the robustness and generalization performance compared to the state-of-the-art on several benchmark datasets.

Takeaways, Limitations

Takeaways:
Overcoming the special model architecture dependency of Limitations of existing TTT methods.
Achieving effective alignment between supervised and self-supervised learning encoders using multimodal contrastive learning techniques.
Reducing gradient interference and maintaining robustness of self-supervised learning.
Improved semantics and performance of test time updates.
Achieving SOTA performance on multiple benchmark datasets.
Limitations:
Further analysis of the generalization performance of the proposed CTA method is needed.
There is a need to expand experimental verification for various data distribution changes.
Further research is needed on its effectiveness and efficiency in practical applications.
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