When Test-Time Adaptation Meets Self-Supervised Models

• URL: http://arxiv.org/abs/2506.23529v1
• Date: Mon, 30 Jun 2025 05:36:01 GMT
• Title: When Test-Time Adaptation Meets Self-Supervised Models
• Authors: Jisu Han, Jihee Park, Dongyoon Han, Wonjun Hwang,
• Abstract summary: Training on test-time data enables deep learning models to adapt to dynamic environmental changes.<n>In this paper, we investigate whether test-time adaptation (TTA) methods can continuously improve models trained via self-supervised learning (SSL)<n>We propose a collaborative learning framework that integrates SSL and TTA models, leveraging contrastive learning and knowledge distillation for stepwise representation refinement.
• Score: 15.947147543403185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• CTA: Cross-Task Alignment for Better Test Time Training [10.54024648915477]
  Test-Time Training (TTT) has emerged as an effective method to enhance model robustness.<n>We introduce CTA (Cross-Task Alignment), a novel approach for improving TTT.<n>We show substantial improvements in robustness and generalization over the state-of-the-art on several benchmark datasets.
  arXiv  Detail & Related papers  (2025-07-07T17:33:20Z)
• Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation [67.80294336559574]
  Continual Test Time Adaptation (CTTA) is a task that requires a source pre-trained model to continually adapt to new scenarios.<n>We propose a novel pipeline, Orthogonal Projection Subspace to aggregate online Prior-knowledge, dubbed OoPk.
  arXiv  Detail & Related papers  (2025-06-23T18:17:39Z)
• Continuous Self-Improvement of Large Language Models by Test-time Training with Verifier-Driven Sample Selection [6.471199527741301]
  We introduce a new framework called VDS-TTT - Verifier-Driven Sample Selection for Test-Time Training.<n>We use a learned verifier to score a pool of generated responses and select only from high ranking pseudo-labeled examples for fine-tuned adaptation.<n>We fine-tune only low-rank LoRA adapter parameters, ensuring adaptation efficiency and fast convergence.
  arXiv  Detail & Related papers  (2025-05-26T03:54:47Z)
• Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning [6.1339395157466425]
  Foundational deep learning (DL) models are general models, trained on diverse, diverse, and unlabelled datasets. We introduce Masked Spectrogram Modeling, a novel self-supervised learning approach for pretraining foundational DL models on radio signals.
  arXiv  Detail & Related papers  (2024-11-14T23:56:57Z)
• BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping [64.8477128397529]
  We propose a training-required and training-free test-time adaptation framework. We maintain a light-weight key-value memory for feature retrieval from instance-agnostic historical samples and instance-aware boosting samples. We theoretically justify the rationality behind our method and empirically verify its effectiveness on both the out-of-distribution and the cross-domain datasets.
  arXiv  Detail & Related papers  (2024-10-20T15:58:43Z)
• Training Language Models to Self-Correct via Reinforcement Learning [98.35197671595343]
  Self-correction has been found to be largely ineffective in modern large language models (LLMs) We develop a multi-turn online reinforcement learning approach, SCoRe, that significantly improves an LLM's self-correction ability using entirely self-generated data. We find that SCoRe achieves state-of-the-art self-correction performance, improving the base models' self-correction by 15.6% and 9.1% respectively on MATH and HumanEval.
  arXiv  Detail & Related papers  (2024-09-19T17:16:21Z)
• Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters [65.15700861265432]
  We present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters. To preserve the zero-shot recognition capability of vision-language models, we introduce a Distribution Discriminative Auto-Selector.
  arXiv  Detail & Related papers  (2024-03-18T08:00:23Z)
• Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning [17.980649681325406]
  We present Point-TTA, a novel test-time adaptation framework for point cloud registration (PCR) Our model can adapt to unseen distributions at test-time without requiring any prior knowledge of the test data. During training, our model is trained using a meta-auxiliary learning approach, such that the adapted model via auxiliary tasks improves the accuracy of the primary task.
  arXiv  Detail & Related papers  (2023-08-31T06:32:11Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• Test-Time Adaptation with Perturbation Consistency Learning [32.58879780726279]
  We propose a simple test-time adaptation method to promote the model to make stable predictions for samples with distribution shifts. Our method can achieve higher or comparable performance with less inference time over strong PLM backbones.
  arXiv  Detail & Related papers  (2023-04-25T12:29:22Z)
• Towards Sustainable Self-supervised Learning [193.78876000005366]
  We propose a Target-Enhanced Conditional (TEC) scheme which introduces two components to the existing mask-reconstruction based SSL. First, we propose patch-relation enhanced targets which enhances the target given by base model and encourages the new model to learn semantic-relation knowledge from the base model. Secondly, we introduce a conditional adapter that adaptively adjusts new model prediction to align with the target of different base models.
  arXiv  Detail & Related papers  (2022-10-20T04:49:56Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.