Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification

• URL: http://arxiv.org/abs/2607.00259v1
• Date: Tue, 30 Jun 2026 23:12:36 GMT
• Title: Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification
• Abstract summary: Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data.<n>In the absence of supervision, entropy-based adaptation is fundamentally underconstrained: multiple distinct parameter updates can achieve similarly low entropy while inducing drastically different decision boundaries.<n>We reinterpret TTA through a posterior-inspired lens induced by entropy minimization, where low-entropy solutions define a pseudo-likelihood over parameters.
• Score: 36.471308624288035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data. However, in the absence of supervision, entropy-based adaptation is fundamentally underconstrained: multiple distinct parameter updates can achieve similarly low entropy while inducing drastically different decision boundaries. This phenomenon, known as underspecification, renders standard TTA brittle and prone to collapse into spurious modes. In this work, we reinterpret TTA through a posterior-inspired lens induced by entropy minimization, where low-entropy solutions define a pseudo-likelihood over parameters. Instead of committing to a single point estimate, we introduce a particle-based diversification framework that explores multiple plausible adaptation trajectories simultaneously. Our method can be viewed as a structured exploration of multiple plausible adaptation solutions, implemented through multi-level diversification at the output, parameter, optimizer, and input levels. Crucially, the framework acts as a plug-and-play wrapper compatible with existing TTA methods. Extensive experiments on challenging benchmarks demonstrate consistent gains in stability and robustness, achieving improvements of 3-4% under mixed shifts, 2-3% with batch size one, and 1-2.5% under label shifts, outperforming state-of-the-art baselines. Our results suggest that treating TTA as a multi-hypothesis inference problem, rather than a single-point optimization task, is key to mitigating underspecification and enabling reliable real-world deployment.

Related papers

• Dual Strategies for Test-Time Adaptation [37.36806938657306]
  Conventional test-time adaptation approaches typically adapt the model using only a small fraction of test samples.<n>This paper introduces DualTTA, a novel framework that improves performance under distribution shifts by utilizing a larger and more diverse set of test samples.
  arXiv  Detail & Related papers  (2026-04-19T17:04:36Z)
• Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptation [3.5808917363708743]
  We study test-time adaptation of foundation models for few-shot classification under a completely frozen-model regime.<n>We propose arguably the first training-free inference method that adapts predictions to the new task by performing a change of measure over the latent embedding distribution induced by the encoder.
  arXiv  Detail & Related papers  (2026-02-02T18:17:29Z)
• MoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm [13.63833424954647]
  Test-Time adaptation (TTA) has proven effective in mitigating performance drops under single-domain distribution shifts.<n>We propose MoETTA, a novel entropy-based TTA framework that integrates the Mixture-of-Experts architecture.
  arXiv  Detail & Related papers  (2025-11-14T10:24:06Z)
• Adapt in the Wild: Test-Time Entropy Minimization with Sharpness and Feature Regularization [85.50560211492898]
  Test-time adaptation (TTA) may fail to improve or even harm the model performance when test data have mixed distribution shifts.<n>This is often a key obstacle preventing existing TTA methods from being deployed in the real world.<n>We propose a sharpness-aware and reliable entropy minimization method, called SAR, for stabilizing TTA from two aspects.
  arXiv  Detail & Related papers  (2025-09-05T10:03:00Z)
• Advancing Reliable Test-Time Adaptation of Vision-Language Models under Visual Variations [67.35596444651037]
  Vision-language models (VLMs) exhibit remarkable zero-shot capabilities but struggle with distribution shifts in downstream tasks when labeled data is unavailable.<n>We propose a Reliable Test-time Adaptation (ReTA) method that enhances reliability from two perspectives.
  arXiv  Detail & Related papers  (2025-07-13T05:37:33Z)
• ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts [71.91042186338163]
  ALoRE is a novel PETL method that reuses the hypercomplex parameterized space constructed by Kronecker product to Aggregate Low Rank Experts.<n>Thanks to the artful design, ALoRE maintains negligible extra parameters and can be effortlessly merged into the frozen backbone.
  arXiv  Detail & Related papers  (2024-12-11T12:31:30Z)
• ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance [18.055032898349438]
  Test time adaptation (TTA) equips deep learning models to handle unseen test data that deviates from the training distribution. We introduce ETAGE, a refined TTA method that integrates entropy minimization with gradient norms and PLPD. Our method prioritizes samples that are less likely to cause instability by combining high entropy with high gradient norms out of adaptation.
  arXiv  Detail & Related papers  (2024-09-14T01:25:52Z)
• Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting [65.21599711087538]
  Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample.<n>Prior methods perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications.<n>We propose an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples.
  arXiv  Detail & Related papers  (2024-03-18T05:49:45Z)
• Persistent Test-time Adaptation in Recurring Testing Scenarios [12.024233973321756]
  Current test-time adaptation (TTA) approaches aim to adapt a machine learning model to environments that change continuously. Yet, it is unclear whether TTA methods can maintain their adaptability over prolonged periods. We propose persistent TTA (PeTTA) which senses when the model is diverging towards collapse and adjusts the adaptation strategy.
  arXiv  Detail & Related papers  (2023-11-30T02:24:44Z)
• On Pitfalls of Test-Time Adaptation [82.8392232222119]
  Test-Time Adaptation (TTA) has emerged as a promising approach for tackling the robustness challenge under distribution shifts. We present TTAB, a test-time adaptation benchmark that encompasses ten state-of-the-art algorithms, a diverse array of distribution shifts, and two evaluation protocols.
  arXiv  Detail & Related papers  (2023-06-06T09:35:29Z)
• Towards Stable Test-Time Adaptation in Dynamic Wild World [60.98073673220025]
  Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. Online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real world.
  arXiv  Detail & Related papers  (2023-02-24T02:03:41Z)

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.