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# BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation | ||
Will be updated. | ||
The code will be updated soon. | ||
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<img width="928" alt="image" src="https://github.com/daeunni/BECoTTA/assets/62705839/511417bf-bb8b-46c2-81ec-7ab9c208b92e"> | ||
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## Abstract | ||
Continual Test Time Adaptation (CTTA) is required to adapt efficiently to continuous unseen domains while retaining previously learned knowledge. However, despite the progress of | ||
CTTA, forgetting-adaptation trade-offs and efficiency are still unexplored. Moreover, current CTTA scenarios assume only the disjoint situation, even though real-world domains are seamlessly changed. | ||
To tackle these challenges, this paper proposes BECoTTA, an input-dependent yet efficient framework for CTTA. We propose Mixture-of-Domain Low-rank Experts (MoDE) that contains two core components: i) Domain-Adaptive Routing, which aids in selectively capturing the domain-adaptive knowledge with multiple domain routers, and (ii) Domain-Expert Synergy Loss to maximize the dependency between each domain and expert. We validate our method outperforms multiple CTTA scenarios including disjoint and gradual domain shits, while only requiring ∼98% fewer trainable parameters. We also provide analyses of our method, including the construction of experts, the effect of domain-adaptive experts, and visualizations. | ||
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