Training Flow Matching Models with Reliable Labels via Self-Purification
This addresses label noise issues in machine learning datasets, particularly for flow-matching models, but appears incremental as it builds on existing frameworks.
The paper tackles the problem of training datasets with mislabeled samples by introducing Self-Purifying Flow Matching (SPFM), which filters unreliable data within the flow-matching framework, resulting in models that generate accurate samples even with noisy labels and surpassing baselines on the TITW dataset.
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.