Deep multi-view representation learning for social imagesMulti-view learningImage embeddingRepresentation learningStacked autoencoderMulti-view representation learning for social images has recently made remarkable achievements in many tasks, such as cross-view classification and cross-modal retrieval. Since ...
Deep Partial Multi-View Learning Changqing.Zhang,Yajie.Cui,Zongbo.Han,... - 《IEEE Transactions on Pattern Analysis & Machine Intelligence》 - 2022 - 被引量: 0 MULTI-VIEW ROBUST REPRESENTATION LEARNING Lusi Li - 《IEEE Transactions on Pattern Analysis & Machine Intelligence》 - 2021 - 被引量...
Few-Shot Food Recognition via Multi-View Representation Learning This article considers the problem of few-shot learning for food recognition. Automatic food recognition can support various applications, e.g., dietary as... S Jiang,W Min,Y Lv,... - 《Acm Transactions on Multimedia Computing Com...
关键字:多视图学习(Multi-view Learning)、多视图表示学习(Multi-view Representation Learning)、孪生网络(Siamese Network)、对抗学习(Adversarial Training)、一致性(Consensus)、互补性(Complementarity)、半监督学习、密度峰值聚类(Density Peak Clustering)。 1. Introduction 多视图学习及多视图表示学习的研究意义,重要...
3.1 Multi-View Deep Discriminant Representation Learning (MDDRL) 在表示学习和分类骨干的基础上,MDDRL结合了两个组件来提高其性能,即深度度量学习以及正交性和对抗性相似性约束。因此,MDDRL的损失函数包含三部分,并被表示为分类损失的加权Lc、约束损失(Ldiff+Ladv)和深度度量学习的对比性损失Lcon ...
Various factors, such as identities, views (poses), and illuminations, are coupled in face images. Disentangling the identity and view representations is a major challenge in face recognition. Existing face recognition systems either use handcrafted feat
这部分对应于S2DMVSC中的“Representation learning part”,以及连接“view-shared self-expressive layer”的部分,并且通过“view-shared self-expressive layer”可以得出本轮的C矩阵,从而提供给“Spectral clustering part”用于自监督信息的生成。 -监督信息的生成和使用 ...
然后从理论上证明了习得隐含表示(latent representation)的通用性; 为了完备性,通过模拟数据传输,将学习潜在视图表示的任务具体转化为退化过程,这样就可以隐式的实现不同视图之间一致性和互补性的最佳权衡。 我们的模型采用对抗性策略,稳定地对缺失视图进行归因,将每个样本的所有视图信息编码为潜在表示,进一步增强了完整性...
参考:多视图子空间聚类/表示学习(Multi-view Subspace Clustering/Representation Learning) ,关于“On the eigenvectors of p-Laplacian”目标函数的优化问题 - 凯鲁嘎吉 - 博客园 3.3 基于子空间聚类(Subspace Clustering, SC)的深度聚类 参考:深度多视图子空间聚类,多视图子空间聚类/表示学习(Multi-view Subspace Clu...
论文标题:Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs论文作者:Zongmo Huang、Yazhou Ren、Xiaorong Pu、Lifang He论文来源:2022, ArXiv论文地址:download 论文代码:download 1 Introduction隶属于多视图聚类(MVC)算法,本文出发点是同时考虑特征信息和结构信息。