Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios

• URL: http://arxiv.org/abs/2505.21387v1
• Date: Tue, 27 May 2025 16:16:54 GMT
• Title: Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
• Authors: Xihong Yang, Siwei Wang, Fangdi Wang, Jiaqi Jin, Suyuan Liu, Yue Liu, En Zhu, Xinwang Liu, Yueming Jin,
• Abstract summary: We propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC.<n>Specifically, we reformulate noisy identification as an anomaly identification problem using GMM.<n>We then design a hybrid rectification strategy to mitigate the adverse effects of noisy data based on the identification results.
• Score: 76.02688769599686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source information from diverse views in recent years. Most existing methods rely on the assumption of clean views. However, noise is pervasive in real-world scenarios, leading to a significant degradation in performance. To tackle this problem, we propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC. Specifically, we reformulate noisy identification as an anomaly identification problem using GMM. We then design a hybrid rectification strategy to mitigate the adverse effects of noisy data based on the identification results. Furthermore, we introduce a noise-robust contrastive mechanism to generate reliable representations. Additionally, we provide a theoretical proof demonstrating that these representations can discard noisy information, thereby improving the performance of downstream tasks. Extensive experiments on six benchmark datasets demonstrate that AIRMVC outperforms state-of-the-art algorithms in terms of robustness in noisy scenarios. The code of AIRMVC are available at https://github.com/xihongyang1999/AIRMVC on Github.

Related papers

• Incomplete Multi-view Clustering via Diffusion Contrastive Generation [10.303281347345955]
  We propose a novel IMVC method called Diffusion Contrastive Generation (DCG)<n>DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data.<n>It integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data.
  arXiv  Detail & Related papers  (2025-03-12T09:27:25Z)
• Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation [61.64052577026623]
  Real-world multi-view datasets are often heterogeneous and imperfect.<n>We propose a novel robust MVL method (namely RML) with simultaneous representation fusion and alignment.<n>In experiments, we employ it in unsupervised multi-view clustering, noise-label classification, and as a plug-and-play module for cross-modal hashing retrieval.
  arXiv  Detail & Related papers  (2025-03-06T07:01:08Z)
• MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training [9.023648972811458]
  RagVL is a novel framework with knowledge-enhanced reranking and noise-injected training. We instruction-tune the MLLM with a simple yet effective instruction template to induce its ranking ability. For generation, we inject visual noise during training at the data and token levels to enhance the generator's robustness.
  arXiv  Detail & Related papers  (2024-07-31T08:43:17Z)
• Robust Learning under Hybrid Noise [24.36707245704713]
  We propose a novel unified learning framework called "Feature and Label Recovery" (FLR) to combat the hybrid noise from the perspective of data recovery.
  arXiv  Detail & Related papers  (2024-07-04T16:13:25Z)
• SoftPatch: Unsupervised Anomaly Detection with Noisy Data [67.38948127630644]
  This paper considers label-level noise in image sensory anomaly detection for the first time. We propose a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset.
  arXiv  Detail & Related papers  (2024-03-21T08:49:34Z)
• Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances [55.37242480995541]
  We propose to denoise noisy NER data with guidance from a small set of clean instances. Along with the main NER model we train a discriminator model and use its outputs to recalibrate the sample weights. Results on public crowdsourcing and distant supervision datasets show that the proposed method can consistently improve performance with a small guidance set.
  arXiv  Detail & Related papers  (2023-10-25T17:23:37Z)
• Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios [23.43319138048058]
  Multimodal emotion recognition (MER) in practical scenarios is significantly challenged by the presence of missing or incomplete data. Traditional methods have often involved discarding data or substituting data segments with zero vectors to approximate these incompletenesses. We introduce a novel noise-robust MER model that effectively learns robust multimodal joint representations from noisy data.
  arXiv  Detail & Related papers  (2023-09-21T10:49:02Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Improving the Robustness of Summarization Systems with Dual Augmentation [68.53139002203118]
  A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. We first explore the summarization models' robustness against perturbations including word-level synonym substitution and noise. We propose a SummAttacker, which is an efficient approach to generating adversarial samples based on language models.
  arXiv  Detail & Related papers  (2023-06-01T19:04:17Z)

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.