Francisco Messina

MITIGATING DATA REPLICATION IN TEXT-TO-AUDIO GENERATIVE DIFFUSION MODELS THROUGH ANTI-MEMORIZATION GUIDANCE

A persistent challenge in generative audio models is data replication, where the model unintentionally generates parts of its training data during inference. In this work, we address this issue in text-to-audio diffusion models by exploring the use of anti-memorization strategies. We adopt Anti-Memo…