EGC: Image Generation and Classification via a Diffusion Energy-Based Model

Understand

Learning image classification and image generation using the same set of network parameters is a challenging problem.

• Recent advanced approaches perform well in one task often exhibit poor performance in the other.
• This work introduces an energy-based classifier and generator, namely EGC, which can achieve superior performance in both tasks using a single neural network.
• Unlike a conventional classifier that outputs a label given an image (i.e., a conditional distribution $p(y|\mathbf{x})$), the forward pass in EGC is a classifier that outputs a joint distribution $p(\mathbf{x},y)$, enabling an image generator in its backward pass by marginalizing out the label $y$.