標(biāo)題: Titlebook: Unsupervised Domain Adaptation; Recent Advances and Jingjing Li,Lei Zhu,Zhekai Du Book 2024 The Editor(s) (if applicable) and The Author(s [打印本頁(yè)] 作者: Menthol 時(shí)間: 2025-3-21 16:17
書(shū)目名稱Unsupervised Domain Adaptation影響因子(影響力)
作者: anaphylaxis 時(shí)間: 2025-3-21 21:24 作者: Immortal 時(shí)間: 2025-3-22 03:13
Machine Learning: Foundations, Methodologies, and Applicationshttp://image.papertrans.cn/u/image/942522.jpg作者: Intercept 時(shí)間: 2025-3-22 06:02
https://doi.org/10.1007/978-981-97-1025-6Transfer Learning; Adversarial Learning; Source-Free Domain adaptation; Active Domain Adaptation; Unsupe作者: 蠟燭 時(shí)間: 2025-3-22 11:58 作者: 前奏曲 時(shí)間: 2025-3-22 13:48
Jingjing Li,Lei Zhu,Zhekai DuCovers not only conventional domain adaptation, but also source-free domain adaptation and active domain adaptation.Presents unique methods to approach domain adaptation from novel perspectives, which作者: 使隔離 時(shí)間: 2025-3-22 18:49 作者: 攀登 時(shí)間: 2025-3-22 22:25
2730-9908 tween the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents act978-981-97-1027-0978-981-97-1025-6Series ISSN 2730-9908 Series E-ISSN 2730-9916 作者: 警告 時(shí)間: 2025-3-23 02:53
Bi-Classifier Adversarial Learning-Based Unsupervised Domain Adaptation,er focuses on preserving target decision boundaries. Experiments on several domain adaptation benchmarks demonstrate the efficacy of both CGDM and uneven bi-classifier learning in boosting adaptation performance.作者: HIKE 時(shí)間: 2025-3-23 07:50
Source-Free Unsupervised Domain Adaptation,ameter sharing further reduces the number of learnable parameters for efficient adaptation. Model perturbation avoids distorting weights like fine-tuning and is more flexible than only updating batch normalization statistics. Experiments demonstrate the effectiveness of both data and model perturbat作者: 小官 時(shí)間: 2025-3-23 10:15 作者: 放牧 時(shí)間: 2025-3-23 16:05
Book 2024based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents act作者: 捐助 時(shí)間: 2025-3-23 18:08 作者: Mettle 時(shí)間: 2025-3-24 02:05 作者: Peak-Bone-Mass 時(shí)間: 2025-3-24 03:29 作者: Jocose 時(shí)間: 2025-3-24 09:17 作者: 污點(diǎn) 時(shí)間: 2025-3-24 12:11 作者: 不自然 時(shí)間: 2025-3-24 16:21 作者: antenna 時(shí)間: 2025-3-24 19:36
Active Learning for Unsupervised Domain Adaptation,roduces two novel techniques to address key limitations of existing active domain adaptation (ADA) methods: estimating target representativeness without source data access and probabilistic uncertainty estimation. First, an energy-based criterion is proposed for selecting representative target sampl作者: Genistein 時(shí)間: 2025-3-25 03:13
Continual Test-Time Unsupervised Domain Adaptation,main data during inference with a continuously changing data distribution. Previous methods have been found to lack noise robustness, leading to a significant increase in errors under strong noise. In this chapter, we address the noise robustness problem in continual TTA by offering three effective 作者: debris 時(shí)間: 2025-3-25 05:17 作者: tympanometry 時(shí)間: 2025-3-25 11:24 作者: 躺下殘殺 時(shí)間: 2025-3-25 14:17 作者: Deduct 時(shí)間: 2025-3-25 18:19 作者: TRAWL 時(shí)間: 2025-3-25 21:20
Criterion Optimization-Based Unsupervised Domain Adaptation,roduce a method called joint causality-invariant feature learning (JCFL) which leverages a Hilbert-Schmidt independence criterion to identify causal factors. Extensive experiments demonstrate that JCFL consistently improves state-of-the-art methods.作者: 統(tǒng)治人類(lèi) 時(shí)間: 2025-3-26 01:19
Continual Test-Time Unsupervised Domain Adaptation,. Finally, to reduce pseudo-label noise, we propose a soft ensemble negative learning mechanism to guide the model optimization using ensemble complementary pseudo-labels. Our method achieves state-of-the-art performance on three widely used continual TTA datasets, particularly in the strong noise setting that we introduced.作者: MOTTO 時(shí)間: 2025-3-26 08:00
2730-9908 to approach domain adaptation from novel perspectives, which.Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received signific作者: 彎彎曲曲 時(shí)間: 2025-3-26 08:35 作者: 不可侵犯 時(shí)間: 2025-3-26 13:15
Unsupervised Domain Adaptation Techniques,ion in areas like computer vision, natural language processing, robotics, and healthcare. This chapter equips readers with a solid understanding of the landscape of unsupervised domain adaptation and sets the context for the in-depth technical chapters that follow.作者: 征稅 時(shí)間: 2025-3-26 17:32
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