{"total":50,"rows":[{"id":"2309.13415","title":"Dream the Impossible: Outlier Imagination with Diffusion Models","created":"2023-09-23","cited_by_count":77,"area":"diffusion"},{"id":"2305.15316","title":"Training on Thin Air: Improve Image Classification with Generated Data","created":"2023-05-24","cited_by_count":50,"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":"2312.03048","title":"DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control","created":"2023-12-05","cited_by_count":45,"area":"diffusion"},{"id":"2304.10535","title":"Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion","created":"2023-04-20","cited_by_count":38,"area":"diffusion"},{"id":"2403.19600","title":"Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model","created":"2024-03-28","cited_by_count":34,"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":"2306.17560","title":"Class-Incremental Learning using Diffusion Model for Distillation and Replay","created":"2023-06-30","cited_by_count":31,"area":"diffusion"},{"id":"2405.03150","title":"Video Diffusion Models: A Survey","created":"2024-05-06","cited_by_count":30,"area":"diffusion"},{"id":"2407.10910","title":"DataDream: Few-shot Guided Dataset Generation","created":"2024-07-15","cited_by_count":29,"area":"diffusion"},{"id":"2403.12803","title":"DreamDA: Generative Data Augmentation with Diffusion Models","created":"2024-03-19","cited_by_count":24,"area":"diffusion"},{"id":"2406.08249","title":"Dataset Enhancement with Instance-Level Augmentations","created":"2024-06-12","cited_by_count":23,"area":"diffusion"},{"id":"2311.11017","title":"Improving Adversarial Transferability by Stable Diffusion","created":"2023-11-18","cited_by_count":22,"area":"diffusion"},{"id":"2304.10278","title":"Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis","created":"2023-04-20","cited_by_count":18,"area":"diffusion"},{"id":"2405.15199","title":"ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models","created":"2024-05-24","cited_by_count":17,"area":"diffusion"},{"id":"2311.18158","title":"HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation","created":"2023-11-30","cited_by_count":16,"area":"diffusion"},{"id":"2410.04810","title":"FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models","created":"2024-10-07","cited_by_count":16,"area":"diffusion"},{"id":"2312.04566","title":"Gen2Det: Generate to Detect","created":"2023-12-07","cited_by_count":12,"area":"diffusion"},{"id":"2312.12080","title":"Learning Subject-Aware Cropping by Outpainting Professional Photos","created":"2023-12-19","cited_by_count":12,"area":"diffusion"},{"id":"2306.04445","title":"Multi-modal Latent Diffusion","created":"2023-06-07","cited_by_count":9,"area":"diffusion"},{"id":"2405.02698","title":"Stable Diffusion Dataset Generation for Downstream Classification Tasks","created":"2024-05-04","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":"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":"2602.19946","title":"When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators","created":"2026-02-23","cited_by_count":6,"area":"diffusion"},{"id":"2405.03243","title":"Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data","created":"2024-05-06","cited_by_count":5,"area":"diffusion"},{"id":"2309.01369","title":"Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation","created":"2023-09-04","cited_by_count":4,"area":"diffusion"},{"id":"2311.03959","title":"Improving the Effectiveness of Deep Generative Data","created":"2023-11-07","cited_by_count":4,"area":"diffusion"},{"id":"2503.09122","title":"Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?","created":"2025-03-12","cited_by_count":4,"area":"diffusion"},{"id":"2503.12563","title":"Diffusion on Graph: Augmentation of Graph Structure for Node Classification","created":"2025-03-16","cited_by_count":4,"area":"diffusion"},{"id":"2505.10551","title":"Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data","created":"2025-05-15","cited_by_count":4,"area":"diffusion"},{"id":"2506.19455","title":"Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation","created":"2025-06-24","cited_by_count":4,"area":"diffusion"},{"id":"2307.12018","title":"GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation Models","created":"2023-07-22","cited_by_count":3,"area":"diffusion"},{"id":"2411.18109","title":"Difficulty Controlled Diffusion Model for Synthesizing Effective Training Data","created":"2024-11-27","cited_by_count":2,"area":"diffusion"},{"id":"2503.09221","title":"Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets","created":"2025-03-12","cited_by_count":2,"area":"diffusion"},{"id":"2504.04582","title":"Your Image Generator Is Your New Private Dataset","created":"2025-04-06","cited_by_count":2,"area":"diffusion"},{"id":"2504.06432","title":"D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition","created":"2025-04-08","cited_by_count":2,"area":"diffusion"},{"id":"2504.12833","title":"SPIE: Semantic and Structural Post-Training of Image Editing Diffusion Models with AI feedback","created":"2025-04-17","cited_by_count":2,"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":"2406.03146","title":"Tiny models from tiny data: Textual and null-text inversion for few-shot distillation","created":"2024-06-05","cited_by_count":1,"area":"diffusion"},{"id":"2407.21016","title":"Add-SD: Rational Generation without Manual Reference","created":"2024-07-30","cited_by_count":1,"area":"diffusion"},{"id":"2505.07481","title":"Addressing degeneracies in latent interpolation for diffusion models","created":"2025-05-12","cited_by_count":1,"area":"diffusion"},{"id":"2401.15282","title":"GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data Synthesis","created":"2024-01-27","cited_by_count":0,"area":"diffusion"},{"id":"2406.01071","title":"Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline","created":"2024-06-03","cited_by_count":0,"area":"diffusion"},{"id":"2506.09955","title":"Canonical Latent Representations in Conditional Diffusion Models","created":"2025-06-11","cited_by_count":0,"area":"diffusion"},{"id":"2509.00177","title":"Category-level Text-to-Image Retrieval Improved: Bridging the Domain Gap with Diffusion Models and Vision Encoders","created":"2025-08-29","cited_by_count":0,"area":"diffusion"},{"id":"2601.21517","title":"HERS: Hidden-Pattern Expert Learning for Risk-Specific Vehicle Damage Adaptation in Diffusion Models","created":"2026-01-29","cited_by_count":0,"area":"diffusion"},{"id":"2603.19575","title":"MagicSeg: Open-World Segmentation Pretraining via Counterfactural Diffusion-Based Auto-Generation","created":"2026-03-20","cited_by_count":0,"area":"diffusion"},{"id":"2603.28763","title":"PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models","created":"2026-03-30","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"}],"next_cursor":null}