Diffusion Models as Data Mining Tools
This provides a scalable visual data mining tool for researchers and practitioners analyzing large, varied image datasets, though it is incremental as it adapts existing generative models for a new application.
The paper tackles the problem of summarizing visual datasets by using finetuned conditional diffusion models to define a typicality measure for visual elements across labels like geography or disease presence, achieving scalability and applicability to diverse datasets including historical and street-view data.
This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. Our insight is that since contemporary generative models learn an accurate representation of their training data, we can use them to summarize the data by mining for visual patterns. Concretely, we show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure on that dataset. This measure assesses how typical visual elements are for different data labels, such as geographic location, time stamps, semantic labels, or even the presence of a disease. This analysis-by-synthesis approach to data mining has two key advantages. First, it scales much better than traditional correspondence-based approaches since it does not require explicitly comparing all pairs of visual elements. Second, while most previous works on visual data mining focus on a single dataset, our approach works on diverse datasets in terms of content and scale, including a historical car dataset, a historical face dataset, a large worldwide street-view dataset, and an even larger scene dataset. Furthermore, our approach allows for translating visual elements across class labels and analyzing consistent changes.