Results 41 to 50 of about 3,036,262 (193)
Spectral Unsupervised Domain Adaptation for Visual Recognition [PDF]
, 2022 Though unsupervised domain adaptation (UDA) has achieved very impressive progress recently, it remains a great challenge due to missing target annotations and the rich discrepancy between source and target distributions.
Lu, Shijian +3 more
core +1 more source
Applied SciencesUnsupervised domain adaptation (UDA) presents a significant challenge in sentiment analysis, especially when faced with differences between source and target domains.
Haidi Badr, Nayer Wanas, Magda Fayek
doaj +1 more source
Remote SensingDeep self-training-based unsupervised domain adaptation (UDA) semantic segmentation methods learn from labeled source domain images and unlabeled target domain images, performing more stably than those based on adversarial training.
Qianqian Liu, Xili Wang
doaj +1 more source
IEEE Access, 2023 LiDAR semantic segmentation is receiving increased attention due to its deployment in autonomous driving applications. As LiDARs come often with other sensors such as RGB cameras, multi-modal approaches for this task have been developed, which however ...
Adriano Cardace +5 more
doaj +1 more source
IEEE Access, 2021 Unsupervised Domain Adaptation (UDA) for re-identification (re-ID) is a challenging task: to avoid a costly annotation of additional data, it aims at transferring knowledge from a domain with annotated data to a domain of interest with only unlabeled ...
Fabian Dubourvieux +4 more
doaj +1 more source
Time Series Domain Adaptation: A Review
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.This survey provides a comprehensive and systematic review of TSDA methods from the perspectives of access‐privacy constraints, category‐space semantics, and source‐target topology, three orthogonal axes that unify existing approaches within a common taxonomy.
M. T. Furqon +2 more
wiley +1 more source
Few-Shot Unsupervised Domain Adaptation via Meta Learning
, 2022 Unsupervised domain adaptation (UDA) has raised a lot of interests in recent years. However, current UDA methods are still not capable enough in dealing with two issues: 1) the scarcity of labeled data in source domain and 2) the need of a general model ...
Chengmei Yang (20259105) +4 more
core +1 more source
Task‐Aligned Haze Removal With Semantic‐Aware Fusion and Contrast Self‐Correction
CAAI Transactions on Intelligence Technology, Volume 11, Issue 4, Page 978-993, August 2026.ABSTRACT Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision‐based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often ...
Jinbin Wang +5 more
wiley +1 more source
Addressing materials’ microstructure diversity using transfer learning
npj Computational Materials, 2022 Materials’ microstructures are signatures of their alloying composition and processing history. Automated, quantitative analyses of microstructural constituents were lately accomplished through deep learning approaches.
Aurèle Goetz +6 more
doaj +1 more source
Simplified Neural Unsupervised Domain Adaptation [PDF]
, 2019 Unsupervised domain adaptation (UDA) is the task of modifying a statistical model trained on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain.
Timothy Miller, Miller, Timothy A
core +1 more source