This article presents OTMatch, a new method for semi-supervised learning that leverages the semantic relationships among classes using an optimal transport loss function. The paper highlights that current algorithms often…
Continue readingThis research paper presents a novel approach to source-free object detection (SFOD), which involves using a pre-trained model on a source domain and unlabeled target domain data. The authors propose…
Continue readingThe article presents SemiReward, a new framework for semi-supervised learning (SSL). The main challenge of SSL is to distinguish high-quality pseudo labels against the confirmation bias. Current pseudo-label selection strategies…
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