{
  "id": 6345,
  "url": "https://arxiv.org/abs/2604.04170v1",
  "title": "Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation",
  "summary": "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 ",
  "authors": "Xu Yan, Jun Yin, Shiliang Sun, Minghua Wan",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-05T16:22:38.000Z",
  "fetched_at": "2026-07-14T16:32:24.292Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6345",
  "original_url": "https://arxiv.org/abs/2604.04170v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}