乐胖代购免代理版

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Previous domain adaptation approaches have predominantly focussed on global domain adaptation, neglecting class\u2010level information and resulting in suboptimal transfer performance. In recent years, a considerable number of researchers have explored class\u2010level domain adaptation, aiming to precisely align the distribution of diverse domains. Nevertheless, existing research on class\u2010level alignment tends to align domain features either on or in proximity to classification boundaries, which introduces ambiguous samples that can impact classification accuracy. In this study, the authors propose a novel strategy called class guided constraints (CGC) to tackle this issue. Specifically, CGC is employed to preserve the compactness within classes and separability between classes of domain features prior to class\u2010level alignment. Furthermore, the authors incorporate CGC in conjunction with similarity guided constraint. 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