arXiv:2307.05601
공식 코드 · Papers with Code
Unsupervised Domain Adaptation with Deep Neural-Network
원문 초록
This report contributes to the field of unsupervised domain adaptation by providing an analysis of existing methods, introducing a new approach, and demonstrating the potential for improving visual recognition tasks across different domains. The results of this study open up opportunities for further study and development of advanced methods in the field of domain adaptation.
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Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition
Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation
Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation
Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting
BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Behavioural Change
토론
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