{
  "annotation_id": "daedf086-27fd-45b1-acc5-03dd3e4e44b8",
  "date_created": "2026-02-17T05:53:16.279000Z",
  "date_modified": "2026-02-17T05:53:16.279000Z",
  "file_hash": "e4086427cfe5ccdc2b6aaafa0434dff23625a6bab57bca53210ffcfbd11054d4",
  "private": false,
  "record": {
    "abstract": "In this paper, we present an automated pipeline for generating domain-specific synthetic datasets with diffusion models, addressing the distribution shift between pre-trained models and real-world deployment environments. Our three-stage framework first synthesizes target objects within domain-specific backgrounds through controlled inpainting. The generated outputs are then validated via a multi-modal assessment that integrates object detection, aesthetic scoring, and vision-language alignment. Finally, a user-preference classifier is employed to capture subjective selection criteria. This pipeline enables the efficient construction of high-quality, deployable datasets while reducing reliance on extensive real-world data collection.",
    "arxiv_id": "2601.08095",
    "authors": [
      "Dongsik Yoon",
      "Jongeun Kim"
    ],
    "categories": [
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "From Prompts to Deployment: Auto-Curated Domain-Specific Dataset Generation via Diffusion Models",
    "url": "https://arxiv.org/abs/2601.08095",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a8446ce2-11b4-4af5-ac9f-4d6358322cb2",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}