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Found 5 papers, 3 papers with code
Incomplete Multi-view Clustering via Prototype-based Imputation
Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC.
Graph Matching with Bi-level Noisy Correspondence
In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC).
Ranked #1 on
Graph Matching
on Willow Object Class
Twin Contrastive Learning for Online Clustering
Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.
Ranked #1 on
Short Text Clustering
on Biomedical
Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification
To solve the TNL problem, we propose a novel method for robust VI-ReID, termed DuAlly Robust Training (DART).
Partially View-aligned Representation Learning with Noise-robust Contrastive Loss
To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.
Contrastive Learning
Partially View-aligned Multi-view Learning
+1
