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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
multi-label classificationdual incomplete multi-view multi-label classificationrepresentation learninglabel correlationsmulti-view consistent representation
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 relying solely on loss-based 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 redundant features. 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.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes a multi-view shared codebook mechanism combined with fused-teacher self-distillation to address dual-missing scenarios where both views and labels are incomplete. It resides in the 'Codebook and Self-Distillation Frameworks' leaf, which contains only two papers in the entire taxonomy of fifty works. This indicates a relatively sparse and emerging research direction within the broader field of incomplete multi-view multi-label classification, suggesting the approach explores a less crowded methodological space compared to more established branches like contrastive learning or label recovery.

The taxonomy reveals that neighboring leaves include 'Weighted and Adaptive Fusion Mechanisms' and 'Instance-Level and Dual-Level Contrastive Learning', which collectively house more papers and represent more mature research directions. The paper's focus on discrete codebook representations and self-distillation diverges from these neighbors by emphasizing structured semantic alignment through limited shared embeddings rather than loss-based contrastive constraints. The taxonomy's scope notes clarify that methods without codebook mechanisms or distillation belong elsewhere, positioning this work at the intersection of representation learning and knowledge transfer under dual incompleteness.

Among thirty candidates examined, the first contribution on multi-view shared codebooks shows one refutable candidate out of ten examined, suggesting some prior overlap in discrete representation learning for multi-view scenarios. The second contribution on label-correlation-oriented weighted fusion and the third on fused-teacher self-distillation each examined ten candidates with zero refutations, indicating these aspects appear more novel within the limited search scope. The statistics suggest that while codebook-based representation learning has some precedent, the integration with correlation-aware fusion and self-distillation may offer incremental novelty given the examined literature.

Based on the top-thirty semantic matches and taxonomy structure, the work appears to occupy a relatively underexplored niche combining discrete codebooks with self-distillation for dual-missing data. The limited search scope and sparse taxonomy leaf suggest potential novelty, though the single refutable candidate for the codebook contribution indicates some methodological overlap exists. A more exhaustive literature review would be needed to fully assess the originality of integrating these components within the dual-missing multi-view multi-label setting.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: incomplete multi-view multi-label classification with dual missing data. This field addresses scenarios where both view features and label annotations are simultaneously incomplete, requiring methods that can handle partial observations across multiple modalities and label spaces. The taxonomy reveals six main branches reflecting diverse solution strategies. Representation Learning and Feature Fusion Approaches focus on extracting and integrating complementary information from available views, often through deep architectures that learn robust joint representations despite missing modalities. Contrastive and Consistency Learning Methods leverage agreement principles and self-supervised signals to align representations across views and enforce semantic coherence, as seen in works like Dual Contrastive Framework[5] and Consistent Representation Learning[38]. Label Recovery and Correlation Exploitation targets the missing label problem by exploiting label dependencies and semantic structures, with methods such as Label Recovery Correlation[32] and Bi-directional Matrix Completion[23]. View Imputation and Reconstruction Techniques explicitly reconstruct absent views or features, exemplified by Attention Embedding Imputation[10] and Task Augmented Imputation[33]. Specialized Learning Scenarios and Extensions address particular challenges like noisy labels, imbalanced data, or active learning settings. Finally, Codebook and Self-Distillation Frameworks employ discrete representation learning and knowledge distillation to capture compact semantic information and transfer knowledge across incomplete observations. Recent work has increasingly explored the interplay between view-level and label-level incompleteness, with many studies combining multiple strategies to achieve robustness. A particularly active line involves self-distillation and codebook mechanisms that distill knowledge from complete to incomplete instances, enabling more stable learning under severe missingness. Shared Codebook Fused Teacher[0] falls squarely within this Codebook and Self-Distillation branch, emphasizing the use of shared discrete codebooks to fuse information from a teacher model that guides learning despite dual missing data. This approach contrasts with nearby methods like View Translation Pseudo Label[3], which focuses on translating between views to generate pseudo-labels, and Self-Adaptive Correlations[1], which adaptively models inter-view and inter-label correlations. While these neighbors address similar dual-missing challenges, Shared Codebook Fused Teacher[0] distinguishes itself by leveraging codebook-based distillation to maintain semantic consistency and transfer robust representations, reflecting a growing trend toward discrete latent representations and teacher-student paradigms in handling complex incomplete data scenarios.

Claimed Contributions

Multi-view shared codebook for discrete consistent representation learning

The authors introduce a structured mechanism using a multi-view shared codebook that quantizes continuous features into discrete representations. This design naturally aligns different views within limited shared codebook embeddings, reduces redundant features, and enhances multi-view consistency through cross-view reconstruction.

10 retrieved papers
Can Refute
Label-correlation-oriented weighted fusion strategy

The authors design a weight estimation method that evaluates how well each view preserves label correlation structures. This method assigns weights to enhance fused prediction quality without relying on additional external networks or learnable weights, fully exploiting structural information in supervision signals.

10 retrieved papers
Fused-teacher self-distillation framework

The authors propose a self-distillation framework where the fused prediction serves as a teacher signal to guide view-specific classifiers. This feeds global knowledge integrated across views back into single-view branches, improving consistency, robustness, and generalization under missing-label conditions.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Multi-view shared codebook for discrete consistent representation learning

The authors introduce a structured mechanism using a multi-view shared codebook that quantizes continuous features into discrete representations. This design naturally aligns different views within limited shared codebook embeddings, reduces redundant features, and enhances multi-view consistency through cross-view reconstruction.

Contribution

Label-correlation-oriented weighted fusion strategy

The authors design a weight estimation method that evaluates how well each view preserves label correlation structures. This method assigns weights to enhance fused prediction quality without relying on additional external networks or learnable weights, fully exploiting structural information in supervision signals.

Contribution

Fused-teacher self-distillation framework

The authors propose a self-distillation framework where the fused prediction serves as a teacher signal to guide view-specific classifiers. This feeds global knowledge integrated across views back into single-view branches, improving consistency, robustness, and generalization under missing-label conditions.

Table of Contents

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