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20122023
most citedReconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks

73 citations · 215 across the 34 of their papers we have counts for

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Showing 2022Show all

13 papers · 1 filter

cs.CV2022★ 4 cited

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…

cs.CV2022★ 3 cited

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…

cs.CV2022★ 1 cited

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…

cs.CV2022

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…

cs.LG2022★ 73 cited

Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks

Jiaqiang Zhang, Senzhang Wang, Songcan Chen

Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging d…

cs.LG2022★ 1 cited

Explicit View-labels Matter: A Multifacet Complementarity Study of Multi-view Clustering

Chuanxing Geng, Aiyang Han, Songcan Chen

Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC). Recently with the introduction of popular contrastive learning, the consistency le…