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Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels
Zhongchen Ma, Lisha Li, Qirong Mao +1
Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-class classification or unsupervised learning. However, these…
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition
Enhao Zhang, Chuanxing Geng, Songcan Chen
Data augmentation for minority classes is an effective strategy for long-tailed recognition, thus developing a large number of methods. Although these methods all ensure the balanc…
Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox
Yunyun Wang, Yao Liu, Songcan Chen
Universal domain adaptation (UniDA) is a general unsupervised domain adaptation setting, which addresses both domain and label shifts in adaptation. Its main challenge lies in how…
Dual-Correction Adaptation Network for Noisy Knowledge Transfer
Yunyun Wang, Weiwen Zheng, Songcan Chen
Previous unsupervised domain adaptation (UDA) methods aim to promote target learning via a single-directional knowledge transfer from label-rich source domain to unlabeled target d…