Partial label learning (PLL) is a weakly supervised learning framework, in which each sample is provided with multiple candidate labels while only one of them is correct. Most of the existing methods are designe
Gmss: Graph-based multi-task self-supervised learning for eeg emotion recognition IEEE Transactions on Affective Computing, 14 (2023), pp. 2512-2525 CrossrefView in ScopusGoogle Scholar Li, Fan, et al., 2024 W. Li, L. Fan, S. Shao, A. Song Generalized contrastive partial label learning...
最新的sota基本都是用到adversarial learning方法，最开始的思想借鉴的GAN。
In partial transfer learning, another more difficult challenge is that we even do not know which part of the source domain label space Cs is shared with the target domain label space Ct because Ct is not accessible during training, which results in two technical difficulties. On one hand, ...
Multi-view multi-behavior interest learning network and contrastive learning for multi-behavior recommendation Knowledge-Based Systems, Volume 305, 2024, Article 112604 Jieyang Su,…, Chen Dong Deceptive evidence detection of belief functions based on reinforcement learning in partial label environment Knowl...
The adversarial objective function corresponding to a targeted attack towards target classyon inputXis the binary cross entropy loss with labely: $${J}_{targeted}({{{\bf{X}}},\,y)=-\log ({P}_{ens}(y|{{{\bf{X}}}))$$ (2) Similarly, the adversarial...
169. Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks会议:CVPR 2018.作者:Wei Xiong, Wenhan Luo, Lin Ma, Wei Liu, Jiebo Luo链接:openaccess.thecvf.com/c170. Partial Transfer Learning With Selective Adversarial Networks会议:CVPR 2018.作者:Zhangjie Cao,...
In terms of training environments, human perception is immersed in a rich multi-sensory, dynamical, three- dimensional experience, whereas standard training sets for ANNs consist of static images curated by human photographers20. While these differences in architecture, environment, and learning proce-...
[4], we hypothesize that thermal images are partial-informative visible images, and thus, the feature extractor for visible images can be generalized to thermal images directly. Therefore, we design a one-stream network feature extractor to get deep features from multi-modality images. It means ...
In terms of training environments, human perception is immersed in a rich multi-sensory, dynamical, three- dimensional experience, whereas standard training sets for ANNs consist of static images curated by human photographers20. While these differences in architecture, environment, and learning proce-...