From Prompts to Deployment: Auto-Curated Domain-Specific Dataset Generation via Diffusion Models

• URL: http://arxiv.org/abs/2601.08095v1
• Date: Tue, 13 Jan 2026 00:29:25 GMT
• Title: From Prompts to Deployment: Auto-Curated Domain-Specific Dataset Generation via Diffusion Models
• Authors: Dongsik Yoon, Jongeun Kim,
• Abstract summary: Our framework first synthesizes target objects within domain-specific backgrounds through controlled inpainting.<n>The generated outputs are then validated via a multi-modal assessment that integrates object detection, aesthetic scoring, and vision-language alignment.<n>This pipeline enables the efficient construction of high-quality, deployable datasets while reducing reliance on extensive real-world data collection.
• Score: 2.101267270902429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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