Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909768252129280 |
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| author | Upadhyay, Uddeshya |
| author_facet | Upadhyay, Uddeshya |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_03012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Upadhyay, Uddeshya Robotics Computer Vision and Pattern Recognition 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. |
| title | Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.03012 |