Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression

• URL: http://arxiv.org/abs/2509.03012v1
• Date: Wed, 03 Sep 2025 04:41:43 GMT
• Title: Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression
• Authors: Uddeshya Upadhyay,
• Abstract summary: Deep neural networks (DNNs) are increasingly being used in autonomous systems.<n>DNNs do not generalize well to domain shift.<n>Recent work on test-time training proposes methods that adapt to a new test distribution on the fly.
• Score: 3.316593788543852
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks (DNNs) are increasingly being used in autonomous systems. However, DNNs do not generalize well to domain shift. Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous systems deployed to the real world. Recent work on test-time training proposes methods that adapt to a new test distribution on the fly by optimizing the DNN model for each test input using self-supervision. However, these techniques result in a sharp increase in inference time as multiple forward and backward passes are required for a single test sample (for test-time training) before finally making the prediction based on the fine-tuned features. This is undesirable for real-world robotics applications where these models may be deployed to resource constraint hardware with strong latency requirements. In this work, we propose a new framework (called UT$^3$) that leverages test-time training for improved performance in the presence of continuous domain shift while also decreasing the inference time, making it suitable for real-world applications. Our method proposes an uncertainty-aware self-supervision task for efficient test-time training that leverages the quantified uncertainty to selectively apply the training leading to sharp improvements in the inference time while performing comparably to standard test-time training protocol. Our proposed protocol offers a continuous setting to identify the selected keyframes, allowing the end-user to control how often to apply test-time training. We demonstrate the efficacy of our method on a dense regression task - monocular depth estimation.

Related papers

• NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training [6.030518150035875]
  This paper introduces a two-stage alignment strategy for EEG foundation models.<n>First, we propose NeuroTTT: a domain-specific self-supervised fine-tuning paradigm.<n>Second, we perform self-supervised test-time training on individual unlabeled test samples.<n>Our approach is the first to unify domain-tuned self-supervision with test-time training in large-scale EEG foundation models.
  arXiv  Detail & Related papers  (2025-09-30T14:14:46Z)
• TAPS : Frustratingly Simple Test Time Active Learning for VLMs [0.0]
  Test-Time Optimization enables models to adapt to new data during inference by updating parameters on-the-fly.<n>We propose a novel Test-Time Active Learning framework that adaptively queries uncertain samples and updates prompts dynamically.<n>Our framework provides a practical and effective solution for real-world deployment in safety-critical applications such as autonomous systems and medical diagnostics.
  arXiv  Detail & Related papers  (2025-07-26T18:04:49Z)
• Test-time Offline Reinforcement Learning on Goal-related Experience [50.94457794664909]
  Research in foundation models has shown that performance can be substantially improved through test-time training.<n>We propose a novel self-supervised data selection criterion, which selects transitions from an offline dataset according to their relevance to the current state.<n>Our goal-conditioned test-time training (GC-TTT) algorithm applies this routine in a receding-horizon fashion during evaluation, adapting the policy to the current trajectory as it is being rolled out.
  arXiv  Detail & Related papers  (2025-07-24T21:11:39Z)
• IT$^3$: Idempotent Test-Time Training [95.78053599609044]
  Deep learning models often struggle when deployed in real-world settings due to distribution shifts between training and test data.<n>We present Idempotent Test-Time Training (IT$3$), a novel approach that enables on-the-fly adaptation to distribution shifts using only the current test instance.<n>Our results suggest that idempotence provides a universal principle for test-time adaptation that generalizes across domains and architectures.
  arXiv  Detail & Related papers  (2024-10-05T15:39:51Z)
• Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues.<n>We propose a novel approach to address this issue at test time without requiring retraining.<n>MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• 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)
• MEMO: Test Time Robustness via Adaptation and Augmentation [131.28104376280197]
  We study the problem of test time robustification, i.e., using the test input to improve model robustness. Recent prior works have proposed methods for test time adaptation, however, they each introduce additional assumptions. We propose a simple approach that can be used in any test setting where the model is probabilistic and adaptable.
  arXiv  Detail & Related papers  (2021-10-18T17:55:11Z)
• MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption [69.76837484008033]
  An unresolved problem in Deep Learning is the ability of neural networks to cope with domain shifts during test-time. We combine meta-learning, self-supervision and test-time training to learn to adapt to unseen test distributions. Our approach significantly improves the state-of-the-art results on the CIFAR-10-Corrupted image classification benchmark.
  arXiv  Detail & Related papers  (2021-03-30T09:33:38Z)
• Adaptive Risk Minimization: Learning to Adapt to Domain Shift [109.87561509436016]
  A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. In this work, we consider the problem setting of domain generalization, where the training data are structured into domains and there may be multiple test time shifts. We introduce the framework of adaptive risk minimization (ARM), in which models are directly optimized for effective adaptation to shift by learning to adapt on the training domains.
  arXiv  Detail & Related papers  (2020-07-06T17:59:30Z)

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.