{
  "id": 6609,
  "url": "https://arxiv.org/abs/2603.27987v1",
  "title": "Beyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution Alignment",
  "summary": "The high cost and accessibility problem associated with large datasets hinder the development of large-scale visual recognition systems. Dataset Distillation addresses these problems by synthesizing compact surrogate datasets for efficient training, storage, transfer, and privacy preservation. The existing state-of-the-art diffusion-based dataset distillation methods face three issues: lack of theoretical justification, poor efficiency in scaling to high data volumes, and failure in data-free sc",
  "authors": "Tongfei Liu, Yufan Liu, Bing Li, Weiming Hu",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-30T03:20:27.000Z",
  "fetched_at": "2026-07-14T16:32:37.309Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6609",
  "original_url": "https://arxiv.org/abs/2603.27987v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}