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- Contrastive Clustering862
An online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning and jointly learns representations and cluster assignments in an end-to-end manner is proposed.
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- A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts438
This work proposes a simple yet effective generative model that takes as input noisy text descriptions about an unseen class and generates synthesized visual features for this class and shows that this method consistently outperforms the state of the art on the largest available benchmarks on Text-based Zero-shot Learning.
- COMIC: Multi-view Clustering Without Parameter Selection369
A novel objective function to project raw data into one space in which the projection embraces the geometric consistency (GC) and the cluster assignment consistency (CAC) and could achieve promising performance in learning view-specific representation and eliminating the heterogeneous gaps across different views.
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- You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network296
A novel method, called you only look yourself (YOLY), which could be one of the first unsupervised and untrained neural networks for image dehazing which bypasses the conventional training paradigm of deep models on hazy-clean pairs or a large scale dataset, thus avoids the labor-intensive data collection and the domain shift issue.
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- Partition level multiview subspace clustering277
A unified multiview subspace clustering model is proposed which incorporates the graph learning from each view, the generation of basic partitions, and the fusion of consensus partition which is seamlessly integrated and can be iteratively boosted by each other towards an overall optimal solution.
- Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering267
A novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space is presented and a method to construct a sparse similarity graph, called L2-graph is introduced.
- Deep subspace clustering with sparsity prior267
This paper proposes a novel subspace clustering method -- deeP subspAce clusteRing with sparsiTY prior with PARTY -- based on a new deep learning architecture that explicitly learns to progressively transform input data into nonlinear latent space and to be adaptive to the local and global subspace structure simultaneously.
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- Deep Multiview Clustering by Contrasting Cluster Assignments191
This paper proposes a cross-view contrastive learning (CVCL) method that learns view-invariant representations and produces clustering results by contrasting the cluster assignments among multiple views and provides a theoretical analysis of soft cluster assignment alignment.
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- Connections Between Nuclear-Norm and Frobenius-Norm-Based Representations183
It is proved that when the dictionary can provide enough representative capacity, FNR is exactly NNR even though the data set contains the Gaussian noise, Laplacian noise, or sample-specified corruption and otherwise, F NR and NNR are two solutions on the column space of the dictionary.
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- Twin Contrastive Learning for Online Clustering173
It is found 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.
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- Structured graph learning for clustering and semi-supervised classification162
This paper proposes a graph learning framework to preserve both the local and global structure of data that uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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