65 citations · 90 across the 6 of their papers we have counts for
6 papers · 1 filter
LightBSR: Towards Lightweight Blind Super-Resolution via Discriminative Implicit Degradation Representation Learning
Jiang Yuan, JI Ma, Bo Wang +2
Implicit degradation estimation-based blind super-resolution (IDE-BSR) hinges on extracting the implicit degradation representation (IDR) of the LR image and adapting it to LR imag…
Incomplete Multi-view Clustering via Diffusion Contrastive Generation
Yuanyang Zhang, Yijie Lin, Weiqing Yan +6
Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to ad…
Rethinking Multi-view Representation Learning via Distilled Disentangling
Guanzhou Ke, Bo Wang, Xiaoli Wang +1
Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth…
Disentangling Multi-view Representations Beyond Inductive Bias
Guanzhou Ke, Yang Yu, Guoqing Chao +3
Multi-view (or -modality) representation learning aims to understand the relationships between different view representations. Existing methods disentangle multi-view representatio…
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
Guanzhou Ke, Guoqing Chao, Xiaoli Wang +3
The past two decades have seen increasingly rapid advances in the field of multi-view representation learning due to it extracting useful information from diverse domains to facili…
MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive Fusion
Guanzhou Ke, Yongqi Zhu, Yang Yu
Multi-view representation learning is essential for many multi-view tasks, such as clustering and classification. However, there are two challenging problems plaguing the community…