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of 4
pro vyhledávání: '"Jingjing Li"'
Publikováno v:
ACM Multimedia
Conventional Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain only when data from both domains is simultaneously accessible, which is challenged by the recent Source-free
Publikováno v:
ACM Multimedia
Conventional machine learning models are often vulnerable to samples with different distributions from the ones of training samples, which is known as domain shift. Domain Generalization (DG) challenges this issue by training a model based on multipl
Publikováno v:
ACM Multimedia
In this paper, we present a disentangled representation learning and enhancement network (DRLE-Net) to address the challenging single image de-raining problems, i.e., raindrop and rain streak removal. Specifically, the DRLE-Net is formulated as a mul
Publikováno v:
ACM Multimedia
Domain adaptive semantic segmentation is recognized as a promising technique to alleviate the domain shift between the labeled source domain and the unlabeled target domain in many real-world applications, such as automatic pilot. However, large amou