Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation

• URL: http://arxiv.org/abs/2604.04170v1
• Date: Sun, 05 Apr 2026 16:22:38 GMT
• Title: Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation
• Abstract summary: We introduce a more structured mechanism for consistent representation learning.<n>We learn discrete consistent representations through a multi-view shared codebook and cross-view reconstruction.<n>In addition, we introduce a fused-teacher self-distillation framework.
• Score: 39.27376279295644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although multi-view multi-label learning has been extensively studied, research on the dual-missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contrastive learning or information bottleneck theory to learn consistent representations under missing-view conditions, but loss-based alignment without explicit structural constraints limits the ability to capture stable and discriminative shared semantics. To address this issue, we introduce a more structured mechanism for consistent representation learning: we learn discrete consistent representations through a multi-view shared codebook and cross-view reconstruction, which naturally align different views within the limited shared codebook embeddings and reduce feature redundancy. At the decision level, we design a weight estimation method that evaluates the ability of each view to preserve label correlation structures, assigning weights accordingly to enhance the quality of the fused prediction. In addition, we introduce a fused-teacher self-distillation framework, where the fused prediction guides the training of view-specific classifiers and feeds the global knowledge back into the single-view branches, thereby enhancing the generalization ability of the model under missing-label conditions. The effectiveness of our proposed method is thoroughly demonstrated through extensive comparative experiments with advanced methods on five benchmark datasets. Code is available at https://github.com/xuy11/SCSD.

Related papers

• Towards Generalized Multi-stage Clustering: Multi-view Self-distillation [10.368796552760571]
  Existing multi-stage clustering methods independently learn the salient features from multiple views and then perform the clustering task. This paper proposes a novel multi-stage deep MVC framework where multi-view self-distillation (DistilMVC) is introduced to distill dark knowledge of label distribution.
  arXiv  Detail & Related papers  (2023-10-29T03:35:34Z)
• Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild [9.380271109354474]
  This work proposes a deep MVC framework where data recovery and alignment are fused in a hierarchically consistent way to maximize the mutual information among different views. To the best of our knowledge, this could be the first successful attempt to handle the missing and unaligned data problem separately with different learning paradigms.
  arXiv  Detail & Related papers  (2023-10-28T06:43:57Z)
• Disentangling Multi-view Representations Beyond Inductive Bias [32.15900989696017]
  We propose a novel multi-view representation disentangling method that ensures both interpretability and generalizability of the resulting representations. Our experiments on four multi-view datasets demonstrate that our proposed method outperforms 12 comparison methods in terms of clustering and classification performance.
  arXiv  Detail & Related papers  (2023-08-03T09:09:28Z)
• Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification [78.15629210659516]
  In this paper, we propose an incomplete multi-view missing multi-label classification network named RANK.<n>We break through the view-level weights inherent in existing methods and propose a quality-aware sub-network to dynamically assign quality scores to each view of each sample.<n>Our model is not only able to handle complete multi-view multi-label data, but also works on datasets with missing instances and labels.
  arXiv  Detail & Related papers  (2023-03-30T03:09:25Z)
• 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)
• Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity [61.05259660910437]
  We propose a global consistency and complementarity network (CoCoNet) to learn representations from multiple views. On the global stage, we reckon that the crucial knowledge is implicitly shared among views, and enhancing the encoder to capture such knowledge can improve the discriminability of the learned representations. Lastly on the local stage, we propose a complementarity-factor, which joints cross-view discriminative knowledge, and it guides the encoders to learn not only view-wise discriminability but also cross-view complementary information.
  arXiv  Detail & Related papers  (2022-09-16T09:24:00Z)
• Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval [17.422973861218182]
  Unsupervised image retrieval aims to learn an efficient retrieval system without expensive data annotations. Recent advance proposes deep fully unsupervised image retrieval aiming at training a deep model from scratch to jointly optimize visual features and quantization codes. We propose a novel self-supervised consistent quantization approach to deep fully unsupervised image retrieval, which consists of part consistent quantization and global consistent quantization.
  arXiv  Detail & Related papers  (2022-06-20T14:39:59Z)
• Variational Distillation for Multi-View Learning [104.17551354374821]
  We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
  arXiv  Detail & Related papers  (2022-06-20T03:09:46Z)
• Resolving label uncertainty with implicit posterior models [71.62113762278963]
  We propose a method for jointly inferring labels across a collection of data samples. By implicitly assuming the existence of a generative model for which a differentiable predictor is the posterior, we derive a training objective that allows learning under weak beliefs.
  arXiv  Detail & Related papers  (2022-02-28T18:09:44Z)
• Deep Partial Multi-View Learning [94.39367390062831]
  We propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets) We fifirst provide a formal defifinition of completeness and versatility for multi-view representation. We then theoretically prove the versatility of the learned latent representations.
  arXiv  Detail & Related papers  (2020-11-12T02:29:29Z)

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.