Dual Advancement of Representation Learning and Clustering for Sparse and Noisy Images
- URL: http://arxiv.org/abs/2409.01781v2
- Date: Sat, 14 Sep 2024 06:40:06 GMT
- Title: Dual Advancement of Representation Learning and Clustering for Sparse and Noisy Images
- Authors: Wenlin Li, Yucheng Xu, Xiaoqing Zheng, Suoya Han, Jun Wang, Xiaobo Sun,
- Abstract summary: Sparse and noisy images (SNIs) pose significant challenges for effective representation learning and clustering.
We propose Dual Advancement of Representation Learning and Clustering (DARLC) to enhance the representations derived from masked image modeling.
Our framework offers a comprehensive approach that improves the learning of representations by enhancing their local perceptibility, distinctiveness, and the understanding of relational semantics.
- Score: 14.836487514037994
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Sparse and noisy images (SNIs), like those in spatial gene expression data, pose significant challenges for effective representation learning and clustering, which are essential for thorough data analysis and interpretation. In response to these challenges, we propose Dual Advancement of Representation Learning and Clustering (DARLC), an innovative framework that leverages contrastive learning to enhance the representations derived from masked image modeling. Simultaneously, DARLC integrates cluster assignments in a cohesive, end-to-end approach. This integrated clustering strategy addresses the "class collision problem" inherent in contrastive learning, thus improving the quality of the resulting representations. To generate more plausible positive views for contrastive learning, we employ a graph attention network-based technique that produces denoised images as augmented data. As such, our framework offers a comprehensive approach that improves the learning of representations by enhancing their local perceptibility, distinctiveness, and the understanding of relational semantics. Furthermore, we utilize a Student's t mixture model to achieve more robust and adaptable clustering of SNIs. Extensive experiments, conducted across 12 different types of datasets consisting of SNIs, demonstrate that DARLC surpasses the state-of-the-art methods in both image clustering and generating image representations that accurately capture gene interactions. Code is available at https://github.com/zipging/DARLC.
Related papers
- Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective [52.662463893268225]
Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios.
Existing SHGL methods encounter two significant limitations.
We introduce a novel framework enhanced by rank and dual consistency constraints.
arXiv Detail & Related papers (2024-12-01T09:33:20Z) - Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation [84.45144851024257]
We propose a novel framework that aims to enhance graph contrastive learning by constructing contrastive views with stronger collaborative information via discrete codes.
The core idea is to map users and items into discrete codes rich in collaborative information for reliable and informative contrastive view generation.
arXiv Detail & Related papers (2024-09-09T14:04:17Z) - A Clustering-guided Contrastive Fusion for Multi-view Representation
Learning [7.630965478083513]
We propose a deep fusion network to fuse view-specific representations into the view-common representation.
We also design an asymmetrical contrastive strategy that aligns the view-common representation and each view-specific representation.
In the incomplete view scenario, our proposed method resists noise interference better than those of our competitors.
arXiv Detail & Related papers (2022-12-28T07:21:05Z) - GraphLearner: Graph Node Clustering with Fully Learnable Augmentation [76.63963385662426]
Contrastive deep graph clustering (CDGC) leverages the power of contrastive learning to group nodes into different clusters.
We propose a Graph Node Clustering with Fully Learnable Augmentation, termed GraphLearner.
It introduces learnable augmentors to generate high-quality and task-specific augmented samples for CDGC.
arXiv Detail & Related papers (2022-12-07T10:19:39Z) - ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial
Multi-View Clustering [52.491074276133325]
We propose an augmentation-free graph contrastive learning framework to solve the problem of partial multi-view clustering.
The proposed approach elevates instance-level contrastive learning and missing data inference to the cluster-level, effectively mitigating the impact of individual missing data on clustering.
arXiv Detail & Related papers (2022-03-01T02:32:25Z) - Clustering by Maximizing Mutual Information Across Views [62.21716612888669]
We propose a novel framework for image clustering that incorporates joint representation learning and clustering.
Our method significantly outperforms state-of-the-art single-stage clustering methods across a variety of image datasets.
arXiv Detail & Related papers (2021-07-24T15:36:49Z) - Graph Contrastive Clustering [131.67881457114316]
We propose a novel graph contrastive learning framework, which is then applied to the clustering task and we come up with the Graph Constrastive Clustering(GCC) method.
Specifically, on the one hand, the graph Laplacian based contrastive loss is proposed to learn more discriminative and clustering-friendly features.
On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.
arXiv Detail & Related papers (2021-04-03T15:32:49Z) - Mixing Consistent Deep Clustering [3.5786621294068373]
Good latent representations produce semantically mixed outputs when decoding linears of two latent representations.
We propose the Mixing Consistent Deep Clustering method which encourages representations to appear realistic.
We show that the proposed method can be added to existing autoencoders to further improve clustering performance.
arXiv Detail & Related papers (2020-11-03T19:47:06Z) - Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot
Learning [21.89909688056478]
We propose a new two-level joint idea to augment the generative network with an inference network during training.
This provides strong cross-modal interaction for effective transfer of knowledge between visual and semantic domains.
We evaluate our approach on four benchmark datasets against several state-of-the-art methods, and show its performance.
arXiv Detail & Related papers (2020-07-15T15:34:09Z) - Clustering based Contrastive Learning for Improving Face Representations [34.75646290505793]
We present Clustering-based Contrastive Learning (CCL), a new clustering-based representation learning approach.
CCL uses labels obtained from clustering along with video constraints to learn discnative face features.
arXiv Detail & Related papers (2020-04-05T13:11:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.