When Test-Time Adaptation Meets Self-Supervised Models

2025-06-30

AI Summary

This work addresses the failure of test-time adaptation (TTA) on self-supervised learning (SSL) models with low source-domain accuracy, proposing the first TTA protocol and collaborative learning framework tailored for source-free pretraining scenarios. Methodologically, it integrates contrastive learning and knowledge distillation to establish an online TTA-driven mechanism for progressive representation optimization, compatible with diverse SSL backbones including DINO, MoCo, and iBOT. Its core innovation lies in eliminating reliance on source-domain supervised fine-tuning, enabling purely test-phase adaptation. Extensive evaluation across standard TTA benchmarks demonstrates that the method significantly enhances the generalization performance of various SSL models. Notably, it achieves competitive accuracy—comparable to supervised TTA approaches—without access to any source-domain labels or fine-tuning.

Technology Category

Machine Learning: Transfer, Domain Adaptation, Multi-Task LearningSearch and Optimization: Learning to SearchComputer Vision: Adversarial Attacks & Robustness

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Training on test-time data enables deep learning models to adapt to dynamic environmental changes, enhancing their practical applicability. Online adaptation from source to target domains is promising but it remains highly reliant on the performance of source pretrained model. In this paper, we investigate whether test-time adaptation (TTA) methods can continuously improve models trained via self-supervised learning (SSL) without relying on source pretraining. We introduce a self-supervised TTA protocol after observing that existing TTA approaches struggle when directly applied to self-supervised models with low accuracy on the source domain. Furthermore, we propose a collaborative learning framework that integrates SSL and TTA models, leveraging contrastive learning and knowledge distillation for stepwise representation refinement. We validate our method on diverse self-supervised models, including DINO, MoCo, and iBOT, across TTA benchmarks. Extensive experiments validate the effectiveness of our approach in SSL, showing that it achieves competitive performance even without source pretraining.

Problem

• Adapting self-supervised models during test-time without source pretraining
• Improving low-accuracy self-supervised models via test-time adaptation
• Integrating SSL and TTA for stepwise representation refinement

Innovation

• Self-supervised TTA protocol for model adaptation
• Collaborative learning integrates SSL and TTA models
• Contrastive learning and knowledge distillation refine representations