AI RESEARCH
Efficient Test-Time Adaptation through Latent Subspace Coefficients Search
arXiv CS.LG
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ArXi:2510.11068v3 Announce Type: replace Real-world deployment often exposes models to distribution shifts, making test-time adaptation (TTA) critical for robustness. Yet most TTA methods are unfriendly to edge deployment, as they rely on backpropagation, activation buffering, or test-time mini-batches, leading to high latency and memory overhead. We propose \textbf{ELaTTA} (\textit{Efficient Latent Test-Time Adaptation}), a gradient-free framework for single-instance TTA under strict on-device constraints.