Training Flow Matching Models with Reliable Labels via Self-Purification

• URL: http://arxiv.org/abs/2509.19091v1
• Date: Tue, 23 Sep 2025 14:43:27 GMT
• Title: Training Flow Matching Models with Reliable Labels via Self-Purification
• Authors: Hyeongju Kim, Yechan Yu, June Young Yi, Juheon Lee,
• Abstract summary: We introduce Self-Purifying Flow Matching (SPFM), a principled approach to filtering unreliable data within the flow-matching framework.<n>SPFM identifies suspicious data using the model itself during the training process, bypassing the need for pretrained models or additional modules.<n>Our experiments demonstrate that models trained with SPFM generate samples that accurately adhere to the specified conditioning, even when trained on noisy labels.
• Score: 6.131772929312606
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 can significantly degrade the performance of a trained model. In this work, we introduce Self-Purifying Flow Matching (SPFM), a principled approach to filtering unreliable data within the flow-matching framework. SPFM identifies suspicious data using the model itself during the training process, bypassing the need for pretrained models or additional modules. Our experiments demonstrate that models trained with SPFM generate samples that accurately adhere to the specified conditioning, even when trained on noisy labels. Furthermore, we validate the robustness of SPFM on the TITW dataset, which consists of in-the-wild speech data, achieving performance that surpasses existing baselines.

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