Semi-supervised classification using generative diffusion models

Diffusion models have revolutionised the field of generative machine learning due to their effectiveness in capturing complex, multimodal data distributions. Semi-supervised learning represents a technique that allows us to extract information from a large corpus of unlabelled data, assuming that a small sample of labeled data is provided. Many generative methods have been previously adapted to semi-supervised learning tasks. In this work, we pioneer adapting state-of-the-art generative diffusion models to the problem of semi-supervised image classification. We propose a self-supervised, pseudo-labelling pipeline which uses a diffusion model to learn the conditional probability distribution of neighbouring data points. Preliminary experiments reveal strong performance even when the model is exposed to a very small percentage of labeled data (1%), validating the extraction of information from the unlabelled data. We conclude by conducting a study on the application of diffusion models in the problem of rainfall nowcasting, a precipitation event classification task based on remote sensing data.