Diffusion Models as Data Mining Tools
Ioannis Siglidis, Aleksander Holynski, Alexei A. Efros, Mathieu Aubry, Shiry Ginosar
2024-08-07
Summary
This paper discusses how to use diffusion models, which are typically used for generating images, as tools for visual data mining to find patterns and summarize large datasets.
What's the problem?
When analyzing large sets of images, it can be difficult to identify important patterns or trends. Traditional methods often require comparing many images directly, which can be slow and inefficient. Additionally, existing methods usually focus on just one dataset at a time, limiting their usefulness.
What's the solution?
The authors propose using diffusion models that have been fine-tuned on specific datasets to create a 'typicality measure.' This measure helps determine how common certain visual elements are within the dataset based on various labels (like location or time). By using these models, they can analyze large and diverse datasets without needing to compare every image directly. This method works well across different types of data, such as historical car images or street view photos.
Why it matters?
This research is important because it shows that diffusion models can be more than just image generators; they can also help researchers and analysts better understand large volumes of visual data. By improving how we mine and summarize visual information, this approach can lead to better insights in various fields, including history, geography, and medical imaging.
Abstract
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.