LACON: Training Text-to-Image Model from Uncurated Data

📰 ArXiv cs.AI

LACON trains text-to-image models using uncurated data, challenging the conventional filter-first paradigm

advanced Published 31 Mar 2026

Action Steps

Re-examine the assumption that low-quality raw data is detrimental to model performance
Explore the potential of using uncurated data for text-to-image generation
Implement LACON to train models on uncurated datasets and evaluate performance
Compare results with traditional curated dataset approaches

Who Needs to Know This

ML researchers and engineers benefit from this approach as it potentially unlocks new sources of training data, while data scientists and product managers can leverage this to improve model performance and reduce data curation costs

Key Insight

💡 Uncurated data may hold untapped potential for improving text-to-image generation model performance

Key Takeaways

LACON trains text-to-image models using uncurated data, challenging the conventional filter-first paradigm

Full Article

Title: LACON: Training Text-to-Image Model from Uncurated Data

Abstract:
arXiv:2603.26866v1 Announce Type: cross Abstract: 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-a

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