{
  "id": 7244,
  "url": "https://arxiv.org/abs/2603.13928v1",
  "title": "Discriminative Flow Matching Via Local Generative Predictors",
  "summary": "Traditional discriminative computer vision relies predominantly on static projections, mapping input features to outputs in a single computational step. Although efficient, this paradigm lacks the iterative refinement and robustness inherent in biological vision and modern generative modelling. In this paper, we propose Discriminative Flow Matching, a framework that reformulates classification and object detection as a conditional transport process. By learning a vector field that continuously t",
  "authors": "Om Govind Jha, Manoj Bamniya, Ayon Borthakur",
  "category": "research",
  "topics": "bias-fairness,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-14T12:56:29.000Z",
  "fetched_at": "2026-07-14T16:33:03.575Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7244",
  "original_url": "https://arxiv.org/abs/2603.13928v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}