Diffusion Models as Data Mining Tools

• URL: http://arxiv.org/abs/2408.02752v1
• Date: Sat, 20 Jul 2024 17:14:31 GMT
• Title: Diffusion Models as Data Mining Tools
• Authors: Ioannis Siglidis, Aleksander Holynski, Alexei A. Efros, Mathieu Aubry, Shiry Ginosar,
• Abstract summary: This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. 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. 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.
• Score: 87.77999285241219
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Scaling Laws for the Value of Individual Data Points in Machine Learning [55.596413470429475]
  We introduce a new perspective by investigating scaling behavior for the value of individual data points. We provide learning theory to support our scaling law, and we observe empirically that it holds across diverse model classes. Our work represents a first step towards understanding and utilizing scaling properties for the value of individual data points.
  arXiv  Detail & Related papers  (2024-05-30T20:10:24Z)
• Modified CycleGAN for the synthesization of samples for wheat head segmentation [0.09999629695552192]
  In the absence of an annotated dataset, synthetic data can be used for model development. We develop a realistic annotated synthetic dataset for wheat head segmentation. The resulting model achieved a Dice score of 83.4% on an internal dataset and 83.6% on two external Global Wheat Head Detection datasets.
  arXiv  Detail & Related papers  (2024-02-23T06:42:58Z)
• DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models [61.906934570771256]
  We present a generic dataset generation model that can produce diverse synthetic images and perception annotations. Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module.
  arXiv  Detail & Related papers  (2023-08-11T14:38:11Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• Modeling Entities as Semantic Points for Visual Information Extraction in the Wild [55.91783742370978]
  We propose an alternative approach to precisely and robustly extract key information from document images. We explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities. The proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models.
  arXiv  Detail & Related papers  (2023-03-23T08:21:16Z)
• A Case for Dataset Specific Profiling [0.9023847175654603]
  Data-driven science is an emerging paradigm where scientific discoveries depend on the execution of computational AI models against rich, discipline-specific datasets. With modern machine learning frameworks, anyone can develop and execute computational models that reveal concepts hidden in the data that could enable scientific applications. For important and widely used datasets, computing the performance of every computational model that can run against a dataset is cost prohibitive in terms of cloud resources.
  arXiv  Detail & Related papers  (2022-08-01T18:38:05Z)
• VizAI : Selecting Accurate Visualizations of Numerical Data [2.6039035727217907]
  VizAI is a generative-discriminative framework that first generates various statistical properties of the data. It is linked to a discriminative model that selects the visualization that best matches the true statistics of the data being visualized. VizAI can easily be trained with minimal supervision and adapts to settings with varying degrees of supervision easily.
  arXiv  Detail & Related papers  (2021-11-07T22:05:44Z)
• Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics [118.75207687144817]
  We introduce Data Maps, a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.
  arXiv  Detail & Related papers  (2020-09-22T20:19:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.