Label distribution learning (LDL) is an interpretable and general learning paradigm that has been applied in many real-world applications. In contrast to the simple logical vector in single-label learning (SLL) and multi-label learning (MLL), LDL assigns labels with a description degree to each instance. In practice, two challenges exist in LDL, namely, how to address the dimensional gap problem during the learning process of LDL and how to exactly recover label distributions from existing logical labels, i.e., Label Enhancement (LE). For most existing LDL and LE algorithms, the fact that the dimension of the input matrix is much higher than that of the output one is alway ignored and it typically leads to the dimensional reduction owing to the unidirectional projection. The valuable information hidden in the feature space is lost during the mapping process. To this end, this study considers bidirectional projections function which can be applied in LE and LDL problems simultaneously. More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one. This loss function aims to potentially reconstruct the input data from the output data. Therefore, it is expected to obtain more accurate results. Finally, experiments on several real-world datasets are carried out to demonstrate the superiority of the proposed method for both LE and LDL.
Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1
National Taiwan University（台湾大学）；Far Eastern Memorial Hospital（远东纪念医院）；Heroic Faith Medical Science Co. Ltd.（浩信医疗科技股份有限公司）；Avalanche Computing Inc.（雪崩计算公司）；Academia Sinica（中央研究院）；HCC Healthcare Group（HCC医疗集团）
BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection
School of Tropical Agriculture and Forestry, Hainan University（海南大学热带农林学院）；School of Information and Communication Engineering（信息与通信工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）
D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement
Key Laboratory of DECV, Chongqing University of Posts and Telecommunications（重庆邮电大学信息与通信工程重点实验室）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；School of Informatics, Aristotle University of Thessaloniki（希腊塞萨洛尼基阿里斯托芬大学信息学院）
PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation
Akian College of Science and Engineering, American University of Armenia（阿塞拜疆美国大学科学与工程学院）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）
