A Characteristic Function Approach to Deep Implicit Generative Modeling

Understand

Implicit Generative Models (IGMs) such as GANs have emerged as effective data-driven models for generating samples, particularly images.

• In this paper, we formulate the problem of learning an IGM as minimizing the expected distance between characteristic functions.
• Specifically, we minimize the distance between characteristic functions of the real and generated data distributions under a suitably-chosen weighting distribution.
• This distance metric, which we term as the characteristic function distance (CFD), can be (approximately) computed with linear time-complexity in the number of samples, in contrast with the quadratic-time Maximum Mean Discrepancy (MMD).