  Joint machine learning models that allow synthesizing and classifying data
often offer uneven performance between those tasks or are unstable to train. In
this work, we depart from a set of empirical observations that indicate the
usefulness of internal representations built by contemporary deep
diffusion-based generative models not only for generating but also predicting.
We then propose to extend the vanilla diffusion model with a classifier that
allows for stable joint end-to-end training with shared parameterization
between those objectives. The resulting joint diffusion model outperforms
recent state-of-the-art hybrid methods in terms of both classification and
generation quality on all evaluated benchmarks. On top of our joint training
approach, we present how we can directly benefit from shared generative and
discriminative representations by introducing a method for visual
counterfactual explanations.
