Diffusion probabilistic models have been successfully used to generate data from noise. However, most diffusion models are computationally expensive and difficult to interpret with a lack of theoretical justification. Random feature models on the other hand have gained popularity due to their interpretability but their application to complex machine learning tasks remains limited. In this work, we present a diffusion model-inspired deep random feature model that is interpretable and gives comparable numerical results to a fully connected neural network having the same number of trainable parameters. Specifically, we extend existing results for random features and derive generalization bounds between the distribution of sampled data and the true distribution using properties of score matching. We validate our findings by generating samples on the fashion MNIST dataset and instrumental audio data.
Provable Separations between Memorization and Generalization in Diffusion Models
Department of Industrial Engineering and Management Sciences, Northwestern University（工业工程与管理科学系，西北大学）；Department of Statistics and Data Science, Northwestern University（统计与数据科学系，西北大学）；School of Mathematics, Georgia Institute of Technology（数学系，佐治亚理工学院）
Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis
Department of Computer Science, University of Toronto（计算机科学系，多伦多大学）；Department of Statistical Sciences, University of Toronto（统计科学系，多伦多大学）；Data Science Institute（数据科学研究所）；Vector Institute（向量研究所）；Robotics Institute（机器人研究所）
FADE: Towards Fairness-aware Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models
School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；School of Informatics, Xiamen University（厦门大学信息学院）；Department of Computer Science, Baylor University（贝勒大学计算机科学系）；Departments of Biostatistics and Epidemiology, Harvard University（哈佛大学生物统计学与流行病学系）
