# Classification via score-based generative modelling

arXiv: https://arxiv.org/abs/2207.11091 · HTML: https://mlsanity.com/paper/2207.11091/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 4 of 4)

- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Sliced Score Matching: A Scalable Approach to Density and Score Estimation](https://mlsanity.com/paper/1905.07088/) (2019)
- [Score-Based Generative Classifiers](https://mlsanity.com/paper/2110.00473/) (2021)
- [Score Matched Neural Exponential Families for Likelihood-Free Inference](https://mlsanity.com/paper/2012.10903/) (2020)

## Built on by (most cited first, 4 of 4)

- [Gaussian Joint Embeddings For Self-Supervised Representation Learning](https://mlsanity.com/paper/2603.26799/) (2026)
- [Variational Inference via Smoothed Particle Hydrodynamics](https://mlsanity.com/paper/2407.09186/) (2024)
- [Variational Inference Using Material Point Method](https://mlsanity.com/paper/2407.20287/) (2024)
- [Gaussian Mixture Attention: Linear-Time Sequence Mixing via Probabilistic Latent Routing](https://mlsanity.com/paper/2606.18283/) (2026)

## Abstract (arXiv)

In this work, we investigated the application of score-based gradient learning in discriminative and generative classification settings. Score function can be used to characterize data distribution as an alternative to density. It can be efficiently learned via score matching, and used to flexibly generate credible samples to enhance discriminative classification quality, to recover density and to build generative classifiers. We analysed the decision theories involving score-based representations, and performed experiments on simulated and real-world datasets, demonstrating its effectiveness in achieving and improving binary classification performance, and robustness to perturbations, particularly in high dimensions and imbalanced situations.