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Evidence record 6345 · automatically gathered

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

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

Record details

Published: 5 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

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How to cite this record

ethics.ai (5 April 2026), “Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation,” evidence record 6345, https://ethics.ai/record/6345 (originally published by arXiv).

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