Learning Data Representations with Joint Diffusion Models

TL;DR

This paper introduces a joint diffusion model that combines data generation and classification in a single, stable framework, improving performance and enabling new interpretability methods.

Contribution

The work extends diffusion models with integrated classifiers for stable joint training, outperforming existing hybrid methods in both generation and classification tasks.

Findings

Outperforms state-of-the-art hybrid models in benchmarks

Enables effective visual counterfactual explanations

Achieves stable end-to-end training with shared parameters

Abstract

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.