Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants

This article likely presents a novel approach to generative modeling, focusing on handling data corruption within a black-box setting. The use of 'self-consistent stochastic interpolants' suggests a method for creating models that are robust to noise and able to learn from corrupted data. The research likely explores techniques to improve the performance and reliability of generative models in real-world scenarios where data quality is often compromised.