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Found 9 papers, 2 papers with code
Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval
Thanks to the ISS and CCA, our method could encode the discrimination into the domain-invariant embedding space for unsupervised cross-domain image retrieval.
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.
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
Deep Fair Clustering via Maximizing and Minimizing Mutual Information
Fair clustering aims to divide data into distinct clusters, while preventing sensitive attributes (e. g., gender, race, RNA sequencing technique) from dominating the clustering.
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification
To address this issue, we propose a novel Adaptive Meta-learner via Gradient Similarity (AMGS) method to improve the model generalization ability to a new task.
Contrastive Clustering
In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.
Ranked #3 on
Image Clustering
on STL-10
(using extra training data)
Multiobjective Test Problems with Degenerate Pareto Fronts
The redundancy of some objectives can lead to the multiobjective problem having a degenerate Pareto front, i. e., the dimension of the Pareto front of the $m$-objective problem be less than (m-1).
Kernel Truncated Regression Representation for Robust Subspace Clustering
Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces.
Locally linear representation for image clustering
There are two popular schemes to construct a similarity graph, i. e., pairwise distance based scheme and linear representation based scheme.
