SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations

2026-09-21

AI Summary

本文提出SPeaR方法，通过在预训练网络中插入轻量级可学习模块并直接优化测试流来解决分布偏移问题，无需源数据或监督热身。

Abstract

Test-time adaptation (TTA) addresses distribution shift using only unlabeled test data. Existing methods typically adapt pretrained models by updating their parameters, limiting both what is adapted and where adaptation can occur within the network. We instead keep the pretrained network frozen and steer its intermediate representations. We introduce SPeaR (Steering Primitive for Realigning Representations), which inserts lightweight learnable modules at stage boundaries and optimizes them directly from the test stream, requiring neither source data nor supervised warm-up. Each primitive is optimized using a gated objective that reduces uncertainty only when adaptation is beneficial, along with a diversity regularizer to prevent collapse, and a multi-depth anchor to stabilize adaptation. We show that steering early representations is the most effective strategy, and that the same primitive transfers across convolutional and Transformer architectures. Across CIFAR-10-C, CIFAR-100-C, and ImageNet-C, SPeaR consistently matches or outperforms methods that adapt orders of magnitude more parameters, remains robust across a wide range of batch sizes, and preserves source-domain performance during continual adaptation.

Problem

• Test-time Adaptation
• Distribution Shift
• Unlabeled Test Data
• Pretrained Models

Innovation

• Test-time Adaptation
• Steering Primitives
• Representation Realigning
• Gated Objective
• Diversity Regularizer