# Multi-level Feature Learning for Contrastive Multi-view Clustering

arXiv: https://arxiv.org/abs/2106.11193 · HTML: https://mlsanity.com/paper/2106.11193/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 62 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Self-Supervised Learning by Cross-Modal Audio-Video Clustering](https://mlsanity.com/paper/1911.12667/) (2019)
- [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020)
- [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021)
- [Labelling unlabelled videos from scratch with multi-modal self-supervision](https://mlsanity.com/paper/2006.13662/) (2020)
- [Reconsidering Representation Alignment for Multi-view Clustering](https://mlsanity.com/paper/2103.07738/) (2021)
- [CoMIR: Contrastive Multimodal Image Representation for Registration](https://mlsanity.com/paper/2006.06325/) (2020)
- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder](https://mlsanity.com/paper/1802.02305/) (2018)
- [Vid2CAD: CAD Model Alignment using Multi-View Constraints from Videos](https://mlsanity.com/paper/2012.04641/) (2020)
- [Deep Multimodal Subspace Clustering Networks](https://mlsanity.com/paper/1804.06498/) (2018)
- [Deep Embedded Multi-view Clustering with Collaborative Training](https://mlsanity.com/paper/2007.13067/) (2020)
- [Deep Robust Clustering by Contrastive Learning](https://mlsanity.com/paper/2008.03030/) (2020)
- [Shared Generative Latent Representation Learning for Multi-view Clustering](https://mlsanity.com/paper/1907.09747/) (2019)
- [Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization](https://mlsanity.com/paper/1809.05998/) (2018)

## Built on by (most cited first, 30 of 73)

- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [Self-Supervised Multimodal Learning: A Survey](https://mlsanity.com/paper/2304.01008/) (2023)
- [Deep Multiview Clustering by Contrasting Cluster Assignments](https://mlsanity.com/paper/2304.10769/) (2023)
- [GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering](https://mlsanity.com/paper/2305.06799/) (2023)
- [Contrastive Multi-view Hyperbolic Hierarchical Clustering](https://mlsanity.com/paper/2205.02618/) (2022)
- [DealMVC: Dual Contrastive Calibration for Multi-view Clustering](https://mlsanity.com/paper/2308.09000/) (2023)
- [DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2303.08358/) (2023)
- [Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification](https://mlsanity.com/paper/2303.17117/) (2023)
- [Semantic Invariant Multi-view Clustering with Fully Incomplete Information](https://mlsanity.com/paper/2305.12743/) (2023)
- [Scalable Incomplete Multi-View Clustering with Structure Alignment](https://mlsanity.com/paper/2308.16541/) (2023)
- [Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning](https://mlsanity.com/paper/2404.17340/) (2024)
- [Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning](https://mlsanity.com/paper/2505.11182/) (2025)
- [A Clustering-guided Contrastive Fusion for Multi-view Representation Learning](https://mlsanity.com/paper/2212.13726/) (2022)
- [A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness](https://mlsanity.com/paper/2403.12211/) (2024)
- [MCoCo: Multi-level Consistency Collaborative Multi-view Clustering](https://mlsanity.com/paper/2302.13339/) (2023)
- [MetaViewer: Towards A Unified Multi-View Representation](https://mlsanity.com/paper/2303.06329/) (2023)
- [Efficient Multi-View Graph Clustering with Local and Global Structure Preservation](https://mlsanity.com/paper/2309.00024/) (2023)
- [Rethinking Multi-view Representation Learning via Distilled Disentangling](https://mlsanity.com/paper/2403.10897/) (2024)
- [ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation](https://mlsanity.com/paper/2407.07171/) (2024)
- [Subspace-Contrastive Multi-View Clustering](https://mlsanity.com/paper/2210.06795/) (2022)
- [Multi-view Semantic Consistency based Information Bottleneck for Clustering](https://mlsanity.com/paper/2303.00002/) (2023)
- [Semantically Consistent Multi-view Representation Learning](https://mlsanity.com/paper/2303.04366/) (2023)
- [Contrastive Continual Multi-view Clustering with Filtered Structural Fusion](https://mlsanity.com/paper/2309.15135/) (2023)
- [A Principled Framework for Multi-View Contrastive Learning](https://mlsanity.com/paper/2507.06979/) (2025)
- [Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information](https://mlsanity.com/paper/2309.00474/) (2023)
- [Differentiable Information Bottleneck for Deterministic Multi-view Clustering](https://mlsanity.com/paper/2403.15681/) (2024)
- [Trusted Mamba Contrastive Network for Multi-View Clustering](https://mlsanity.com/paper/2412.16487/) (2024)
- [Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation](https://mlsanity.com/paper/2503.04151/) (2025)
- [Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios](https://mlsanity.com/paper/2505.21387/) (2025)
- [Convex Combination Consistency between Neighbors for Weakly-supervised Action Localization](https://mlsanity.com/paper/2205.00400/) (2022)

## Abstract (arXiv)

Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention. However, existing works punish multiple objectives in the same feature space, where they ignore the conflict between learning consistent common semantics and reconstructing inconsistent view-private information. In this paper, we propose a new framework of multi-level feature learning for contrastive multi-view clustering to address the aforementioned issue. Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces. Specifically, the reconstruction objective is conducted on the low-level features. Two consistency objectives based on contrastive learning are conducted on the high-level features and the semantic labels, respectively. They make the high-level features effectively explore the common semantics and the semantic labels achieve the multi-view clustering. As a result, the proposed framework can reduce the adverse influence of view-private information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness.