AI RESEARCH
LACON: Training Text-to-Image Model from Uncurated Data
arXiv CS.AI
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ArXi:2603.26866v1 Announce Type: cross The success of modern text-to-image generation is largely attributed to massive, high-quality datasets. Currently, these datasets are curated through a filter-first paradigm that aggressively discards low-quality raw data based on the assumption that it is detrimental to model performance. Is the discarded bad data truly useless, or does it hold untapped potential? In this work, we critically re-examine this question. We propose LACON (Labeling-and-Conditioning), a novel.