A Diffusive Classification Loss for Learning Energy-based Generative Models

Understand

Score-based generative models have recently achieved remarkable success.

• While they are usually parameterized by the score, an alternative way is to use a series of time-dependent energy-based models (EBMs), where the score is obtained from the negative input-gradient of the energy.
• Crucially, EBMs can be leveraged not only for generation, but also for tasks such as compositional sampling or building Boltzmann Generators via Monte Carlo methods.
• However, training EBMs remains challenging.