22 citations · 108 across the 19 of their papers we have counts for
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No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning
Xiang Li, Songcan Chen
Label Distribution Learning (LDL) assigns soft labels, a.k.a. degrees, to a sample. In reality, it is always laborious to obtain complete degrees, giving birth to the Incomplete LD…
Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning
Wenhai Wan, Xinrui Wang, Ming-Kun Xie +3
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise. However, a more common scenario in the real world is the presence of both…
Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
Xiang Li, Xinrui Wang, Songcan Chen
In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge,…
Jacobian Norm for Unsupervised Source-Free Domain Adaptation
Weikai Li, Meng Cao, Songcan Chen
Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer knowledge from a well-trained source model to a related but unlabeled target domain. In such a scenario,…
A Similarity-based Framework for Classification Task
Zhongchen Ma, Songcan Chen
Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting i…
Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task
Quan Feng, Songcan Chen
Multi-task learning is to improve the performance of the model by transferring and exploiting common knowledge among tasks. Existing MTL works mainly focus on the scenario where la…