Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering

• URL: http://arxiv.org/abs/2206.02721v1
• Date: Mon, 6 Jun 2022 16:23:05 GMT
• Title: Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
• Authors: Yongyi Su, Xun Xu, Kui Jia
• Abstract summary: We develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domain and match the target clusters to the source ones to improve generalization. We demonstrate that under all TTT protocols TTAC consistently outperforms the state-of-the-art methods on five TTT datasets.
• Score: 37.76664203157892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available and instant inference on target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we further develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domain and match the target clusters to the source ones to improve generalization. Pseudo label filtering and iterative updating are developed to improve the effectiveness and efficiency of anchored clustering. We demonstrate that under all TTT protocols TTAC consistently outperforms the state-of-the-art methods on five TTT datasets. We hope this work will provide a fair benchmarking of TTT methods and future research should be compared within respective protocols. A demo code is available at https://github.com/Gorilla-Lab-SCUT/TTAC.

Related papers

• UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation [66.05528698010697]
  Test-Time Adaptation aims to adapt pre-trained models to the target domain during testing. Researchers have identified various challenging scenarios and developed diverse methods to address these challenges. We propose a Unified Test-Time Adaptation benchmark, which is comprehensive and widely applicable.
  arXiv  Detail & Related papers  (2024-07-29T15:04:53Z)
• Active Test-Time Adaptation: Theoretical Analyses and An Algorithm [51.84691955495693]
  Test-time adaptation (TTA) addresses distribution shifts for streaming test data in unsupervised settings. We propose the novel problem setting of active test-time adaptation (ATTA) that integrates active learning within the fully TTA setting.
  arXiv  Detail & Related papers  (2024-04-07T22:31:34Z)
• pSTarC: Pseudo Source Guided Target Clustering for Fully Test-Time Adaptation [15.621092104244003]
  Test Time Adaptation (TTA) is a pivotal concept in machine learning, enabling models to perform well in real-world scenarios. We propose a novel approach called pseudo Source guided Target Clustering (pSTarC) addressing the relatively unexplored area of TTA under real-world domain shifts.
  arXiv  Detail & Related papers  (2023-09-02T07:13:47Z)
• Improved Test-Time Adaptation for Domain Generalization [48.239665441875374]
  Test-time training (TTT) adapts the learned model with test data. This work addresses two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase. We introduce additional adaptive parameters for the trained model, and we suggest only updating the adaptive parameters during the test phase.
  arXiv  Detail & Related papers  (2023-04-10T10:12:38Z)
• Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training [37.75537703971045]
  We develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. Self-training(ST) has demonstrated great success in learning from unlabeled data. TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets.
  arXiv  Detail & Related papers  (2023-03-20T04:30:18Z)
• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [107.05966685291067]
  We propose test-time prompt tuning (TPT) to learn adaptive prompts on the fly with a single test sample. TPT improves the zero-shot top-1 accuracy of CLIP by 3.6% on average. In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data.
  arXiv  Detail & Related papers  (2022-09-15T17:55:11Z)

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.