Presents a method to train CNF without simulation, training the model by regressing the Vector Field of fixed conditional probability paths. The path is generally expressed as a probability flow (Vector Field) that varies with time .
This approach improves upon the sampling efficiency issues present in existing diffusion models and enables a more efficient generation process by utilizing diverse probability paths.
Flow Matchings