Title: IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: test time adaptation, continual learning

Abstract: Test-time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution.
However, fully leveraging the rich representations of large pretrained models with minimal parameter updates remains underexplored.
In this paper, we propose a novel approach, Intrinsic Mixture of Spectral Experts (IMSE), that leverages the spectral experts inherently embedded in Vision Transformers. 
We decompose each linear layer via singular value decomposition (SVD) and adapt only the singular values, referring to each decomposed rank-1 component as a spectral expert while keeping the singular vectors fixed.
We further identify a key limitation of entropy minimization in TTA: it often reduces feature variance, causing the model to rely on domain-specific cues rather than class-discriminative features. 
To address this, we propose a diversity maximization loss based on singular vector–input alignment, which maximizing diversity of response pattern.
In the continual test-time adaptation (CTTA) scenario, beyond preserving pretrained knowledge, it is crucial to retain and reuse knowledge from previously observed domains. We introduce Domain-Aware Spectral Code Retrieval, which estimates input distributions to detect domain shifts, and retrieves adapted singular values for rapid adaptation.
Extensive experiments show that our method achieves state-of-the-art performance on ImageNet-C/R/A under single-domain TTA. In CTTA, it improves accuracy by 3.4pp with 2,000$\times$ fewer trainable parameters.

TL;DR: We propose a test-time adaptation method that leverages the intrinsic spectral structures of pretrained Vision Transformers, addressing underexplored challenges in both TTA and CTTA.