{"total":28,"rows":[{"id":"2301.13188","title":"Extracting Training Data from Diffusion Models","created":"2023-01-30","cited_by_count":732,"area":"diffusion"},{"id":"2302.07944","title":"Effective Data Augmentation With Diffusion Models","created":"2023-02-07","cited_by_count":283,"area":"diffusion"},{"id":"2212.08420","title":"Fake it till you make it: Learning transferable representations from synthetic ImageNet clones","created":"2022-12-16","cited_by_count":172,"area":"diffusion"},{"id":"2311.17137","title":"Generative Models: What Do They Know? Do They Know Things? Let's Find Out!","created":"2023-11-28","cited_by_count":50,"area":"diffusion"},{"id":"2302.10586","title":"Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels","created":"2023-02-21","cited_by_count":37,"area":"diffusion"},{"id":"2408.14340","title":"Foundation Models for Music: A Survey","created":"2024-08-26","cited_by_count":37,"area":"diffusion"},{"id":"2311.17921","title":"Do text-free diffusion models learn discriminative visual representations?","created":"2023-11-29","cited_by_count":33,"area":"diffusion"},{"id":"2310.06836","title":"A General Protocol to Probe Large Vision Models for 3D Physical Understanding","created":"2023-10-10","cited_by_count":32,"area":"diffusion"},{"id":"2401.12978","title":"Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models","created":"2024-01-23","cited_by_count":32,"area":"diffusion"},{"id":"2306.17560","title":"Class-Incremental Learning using Diffusion Model for Distillation and Replay","created":"2023-06-30","cited_by_count":31,"area":"diffusion"},{"id":"2411.16725","title":"$\\textit{Revelio}$: Interpreting and leveraging semantic information in diffusion models","created":"2024-11-23","cited_by_count":30,"area":"diffusion"},{"id":"2403.12448","title":"Do Generated Data Always Help Contrastive Learning?","created":"2024-03-19","cited_by_count":25,"area":"diffusion"},{"id":"2403.12803","title":"DreamDA: Generative Data Augmentation with Diffusion Models","created":"2024-03-19","cited_by_count":24,"area":"diffusion"},{"id":"2305.10722","title":"Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners","created":"2023-05-18","cited_by_count":14,"area":"diffusion"},{"id":"2408.00350","title":"A Simple Background Augmentation Method for Object Detection with Diffusion Model","created":"2024-08-01","cited_by_count":9,"area":"diffusion"},{"id":"2403.15309","title":"Controlled Training Data Generation with Diffusion Models","created":"2024-03-22","cited_by_count":8,"area":"diffusion"},{"id":"2309.07909","title":"DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation","created":"2023-09-10","cited_by_count":7,"area":"diffusion"},{"id":"2308.06739","title":"Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks","created":"2023-08-13","cited_by_count":6,"area":"diffusion"},{"id":"2311.16122","title":"Semantic Generative Augmentations for Few-Shot Counting","created":"2023-10-26","cited_by_count":6,"area":"diffusion"},{"id":"2408.02752","title":"Diffusion Models as Data Mining Tools","created":"2024-07-20","cited_by_count":5,"area":"diffusion"},{"id":"2311.03959","title":"Improving the Effectiveness of Deep Generative Data","created":"2023-11-07","cited_by_count":4,"area":"diffusion"},{"id":"2406.20085","title":"Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language","created":"2024-06-28","cited_by_count":4,"area":"diffusion"},{"id":"2505.17560","title":"Deeper Diffusion Models Amplify Bias","created":"2025-05-23","cited_by_count":4,"area":"diffusion"},{"id":"2408.14584","title":"DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning","created":"2024-08-26","cited_by_count":3,"area":"diffusion"},{"id":"2310.15524","title":"On the Inherent Privacy Properties of Discrete Denoising Diffusion Models","created":"2023-10-24","cited_by_count":2,"area":"diffusion"},{"id":"2403.15766","title":"BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion","created":"2024-03-23","cited_by_count":0,"area":"diffusion"},{"id":"2412.15358","title":"Dataset Augmentation by Mixing Visual Concepts","created":"2024-12-19","cited_by_count":0,"area":"diffusion"},{"id":"2505.08246","title":"Identifying Memorization of Diffusion Models through $p$-Laplace Analysis: Estimators, Bounds and Applications","created":"2025-05-13","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}