{"total":20,"rows":[{"id":"2306.04542","title":"On the Design Fundamentals of Diffusion Models: A Survey","created":"2023-06-07","cited_by_count":60,"area":"diffusion"},{"id":"2407.00783","title":"Diffusion Models and Representation Learning: A Survey","created":"2024-06-30","cited_by_count":54,"area":"diffusion"},{"id":"2307.00467","title":"MissDiff: Training Diffusion Models on Tabular Data with Missing Values","created":"2023-07-02","cited_by_count":39,"area":"diffusion"},{"id":"2406.15735","title":"Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model","created":"2024-06-22","cited_by_count":28,"area":"diffusion"},{"id":"2310.13545","title":"ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection","created":"2023-10-20","cited_by_count":25,"area":"diffusion"},{"id":"2305.12252","title":"Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model","created":"2023-05-20","cited_by_count":22,"area":"diffusion"},{"id":"2401.11115","title":"MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation","created":"2024-01-20","cited_by_count":10,"area":"diffusion"},{"id":"2312.08825","title":"Guided Diffusion from Self-Supervised Diffusion Features","created":"2023-12-14","cited_by_count":9,"area":"diffusion"},{"id":"2406.01899","title":"Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models","created":"2024-06-04","cited_by_count":8,"area":"diffusion"},{"id":"2410.21967","title":"Dual Conditional Diffusion Models for Sequential Recommendation","created":"2024-10-29","cited_by_count":8,"area":"diffusion"},{"id":"2405.18029","title":"Are Images Indistinguishable to Humans Also Indistinguishable to Classifiers?","created":"2024-05-28","cited_by_count":5,"area":"diffusion"},{"id":"2504.17253","title":"DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks","created":"2025-04-24","cited_by_count":2,"area":"diffusion"},{"id":"2510.10149","title":"Robust Learning of Diffusion Models with Extremely Noisy Conditions","created":"2025-10-11","cited_by_count":2,"area":"diffusion"},{"id":"2505.17783","title":"Generative Data Augmentation for Object Point Cloud Segmentation","created":"2025-05-23","cited_by_count":1,"area":"diffusion"},{"id":"2404.11795","title":"Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning","created":"2024-04-17","cited_by_count":0,"area":"diffusion"},{"id":"2411.15199","title":"Input-Adaptive Generative Dynamics in Diffusion Models","created":"2024-11-19","cited_by_count":0,"area":"diffusion"},{"id":"2605.19289","title":"What Makes Synthetic Data Effective in Image Segmentation","created":"2026-05-19","cited_by_count":0,"area":"diffusion"},{"id":"2605.25210","title":"Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning","created":"2026-05-24","cited_by_count":0,"area":"diffusion"},{"id":"2607.16685","title":"Semi-Supervised Conditional Diffusion via Label Augmentation","created":"2026-07-18","cited_by_count":0,"area":"diffusion"},{"id":"2609.08253","title":"Revisiting Spectral Representations in Generative Diffusion Models","created":"2026-09-08","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}