Training Flow Matching Models with Reliable Labels via Self-Purification
Training datasets are inherently imperfect, often containing mislabeled samples due to human annotation errors, limitations of tagging models, and other sources of noise. Such label contamination...
https://arxiv.org/abs/2509.19091