{"total":91,"rows":[{"id":"2304.08466","title":"Synthetic Data from Diffusion Models Improves ImageNet Classification","created":"2023-04-17","cited_by_count":327,"area":"diffusion"},{"id":"2307.01850","title":"Self-Consuming Generative Models Go MAD","created":"2023-07-04","cited_by_count":250,"area":null},{"id":"2405.17247","title":"An Introduction to Vision-Language Modeling","created":"2024-05-27","cited_by_count":178,"area":"mllm"},{"id":"2401.16380","title":"Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling","created":"2024-01-29","cited_by_count":161,"area":null},{"id":"2308.06038","title":"Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning","created":"2023-08-11","cited_by_count":154,"area":"mllm"},{"id":"2402.07043","title":"A Tale of Tails: Model Collapse as a Change of Scaling Laws","created":"2024-02-10","cited_by_count":130,"area":"synth"},{"id":"2309.14303","title":"Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation","created":"2023-09-25","cited_by_count":112,"area":null},{"id":"2306.07282","title":"Waffling around for Performance: Visual Classification with Random Words and Broad Concepts","created":"2023-06-12","cited_by_count":101,"area":"mllm"},{"id":"2307.10350","title":"Improving Multimodal Datasets with Image Captioning","created":"2023-07-19","cited_by_count":93,"area":"mllm"},{"id":"2302.03298","title":"Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion","created":"2023-02-07","cited_by_count":81,"area":"diffusion"},{"id":"2308.04152","title":"Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions","created":"2023-08-08","cited_by_count":77,"area":"mllm"},{"id":"2309.13415","title":"Dream the Impossible: Outlier Imagination with Diffusion Models","created":"2023-09-23","cited_by_count":77,"area":"diffusion"},{"id":"2306.07180","title":"Diffusion Models for Black-Box Optimization","created":"2023-06-12","cited_by_count":76,"area":"diffusion"},{"id":"2310.15160","title":"FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models","created":"2023-10-23","cited_by_count":61,"area":null},{"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":"2403.16605","title":"SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation","created":"2024-03-25","cited_by_count":41,"area":"diffusion"},{"id":"2405.20469","title":"Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images","created":"2024-05-30","cited_by_count":41,"area":null},{"id":"2310.10402","title":"Real-Fake: Effective Training Data Synthesis Through Distribution Matching","created":"2023-10-16","cited_by_count":36,"area":null},{"id":"2306.07200","title":"Fill-Up: Balancing Long-Tailed Data with Generative Models","created":"2023-06-12","cited_by_count":34,"area":null},{"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":"2310.00158","title":"Feedback-guided Data Synthesis for Imbalanced Classification","created":"2023-09-29","cited_by_count":33,"area":"synth"},{"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":"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":"2311.11017","title":"Improving Adversarial Transferability by Stable Diffusion","created":"2023-11-18","cited_by_count":22,"area":"diffusion"},{"id":"2403.18775","title":"ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object","created":"2024-03-27","cited_by_count":22,"area":"diffusion"},{"id":"2403.06741","title":"Distribution-Aware Data Expansion with Diffusion Models","created":"2024-03-11","cited_by_count":20,"area":"diffusion"},{"id":"2304.10253","title":"Image retrieval outperforms diffusion models on data augmentation","created":"2023-04-20","cited_by_count":14,"area":"diffusion"},{"id":"2310.01830","title":"AI-Generated Images as Data Source: The Dawn of Synthetic Era","created":"2023-10-03","cited_by_count":13,"area":null},{"id":"2403.06168","title":"DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation","created":"2024-03-10","cited_by_count":13,"area":null},{"id":"2410.17606","title":"Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation","created":"2024-10-23","cited_by_count":13,"area":null},{"id":"2411.17687","title":"GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration","created":"2024-11-26","cited_by_count":13,"area":"diffusion"},{"id":"2510.12225","title":"HoneyBee: Data Recipes for Vision-Language Reasoners","created":"2025-10-14","cited_by_count":13,"area":"lr"},{"id":"2305.12799","title":"Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaboration","created":"2023-05-22","cited_by_count":12,"area":"synth"},{"id":"2312.04566","title":"Gen2Det: Generate to Detect","created":"2023-12-07","cited_by_count":12,"area":"diffusion"},{"id":"2406.14551","title":"Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation","created":"2024-06-20","cited_by_count":12,"area":null},{"id":"2405.07925","title":"Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data","created":"2024-05-13","cited_by_count":11,"area":"diffusion"},{"id":"2409.00547","title":"Data Augmentation for Image Classification using Generative AI","created":"2024-08-31","cited_by_count":11,"area":null},{"id":"2402.19472","title":"Efficient Lifelong Model Evaluation in an Era of Rapid Progress","created":"2024-02-29","cited_by_count":10,"area":null},{"id":"2410.01322","title":"Forte : Finding Outliers with Representation Typicality Estimation","created":"2024-10-02","cited_by_count":9,"area":null},{"id":"2412.16156","title":"Personalized Representation from Personalized Generation","created":"2024-12-20","cited_by_count":9,"area":null},{"id":"2412.01137","title":"TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition","created":"2024-12-02","cited_by_count":8,"area":null},{"id":"2310.01330","title":"Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association","created":"2023-10-02","cited_by_count":7,"area":null},{"id":"2309.06358","title":"Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering","created":"2023-09-03","cited_by_count":6,"area":null},{"id":"2311.00056","title":"Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion","created":"2023-10-31","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":"2402.18607","title":"Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective","created":"2024-02-28","cited_by_count":6,"area":"diffusion"},{"id":"2410.13674","title":"Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion","created":"2024-10-17","cited_by_count":6,"area":"diffusion"},{"id":"2411.02592","title":"Decoupled Data Augmentation for Improving Image Classification","created":"2024-10-29","cited_by_count":6,"area":null},{"id":"2510.15783","title":"ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection","created":"2025-10-17","cited_by_count":6,"area":"diffusion"},{"id":"2306.09192","title":"DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers","created":"2023-06-15","cited_by_count":5,"area":null},{"id":"2307.13697","title":"Benchmarking and Analyzing Generative Data for Visual Recognition","created":"2023-07-25","cited_by_count":5,"area":null},{"id":"2403.07356","title":"Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning","created":"2024-03-12","cited_by_count":5,"area":null},{"id":"2409.07003","title":"ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics","created":"2024-09-11","cited_by_count":5,"area":null},{"id":"2410.05057","title":"SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification","created":"2024-10-07","cited_by_count":5,"area":null},{"id":"2410.22944","title":"Focus On This, Not That! Steering LLMs with Adaptive Feature Specification","created":"2024-10-30","cited_by_count":5,"area":null},{"id":"2310.00277","title":"A Unified Framework for Generative Data Augmentation: A Comprehensive Survey","created":"2023-09-30","cited_by_count":4,"area":null},{"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":"2312.05464","title":"Identifying and Mitigating Model Failures through Few-shot CLIP-aided Diffusion Generation","created":"2023-12-09","cited_by_count":3,"area":null},{"id":"2405.13580","title":"AltChart: Enhancing VLM-based Chart Summarization Through Multi-Pretext Tasks","created":"2024-05-22","cited_by_count":3,"area":null},{"id":"2502.01980","title":"Generative Data Mining with Longtail-Guided Diffusion","created":"2025-02-04","cited_by_count":3,"area":"diffusion"},{"id":"2509.22635","title":"Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance","created":"2025-09-26","cited_by_count":3,"area":null},{"id":"2406.12368","title":"MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations","created":"2024-06-18","cited_by_count":2,"area":null},{"id":"2410.04634","title":"Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models","created":"2024-10-06","cited_by_count":2,"area":null},{"id":"2412.09706","title":"Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation","created":"2024-12-12","cited_by_count":2,"area":null},{"id":"2412.17684","title":"COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation","created":"2024-12-23","cited_by_count":2,"area":null},{"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":"2505.21574","title":"Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models","created":"2025-05-27","cited_by_count":2,"area":"diffusion"},{"id":"2606.00571","title":"On the Difficulty of Learning a Meta-network for Training Data Selection","created":"2026-05-30","cited_by_count":2,"area":null},{"id":"2306.01144","title":"Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data","created":"2023-06-01","cited_by_count":1,"area":"lr"},{"id":"2307.08526","title":"Image Captions are Natural Prompts for Text-to-Image Models","created":"2023-07-17","cited_by_count":1,"area":null},{"id":"2407.05546","title":"AID-AppEAL: Automatic Image Dataset and Algorithm for Content Appeal Enhancement and Assessment Labeling","created":"2024-07-08","cited_by_count":1,"area":null},{"id":"2407.14765","title":"Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs","created":"2024-07-20","cited_by_count":1,"area":null},{"id":"2410.06841","title":"Boosting Few-Shot Detection with Large Language Models and Layout-to-Image Synthesis","created":"2024-10-09","cited_by_count":1,"area":"diffusion"},{"id":"2412.10831","title":"Low-Biased General Annotated Dataset Generation","created":"2024-12-14","cited_by_count":1,"area":null},{"id":"2505.04963","title":"ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis","created":"2025-05-08","cited_by_count":1,"area":null},{"id":"2505.11703","title":"LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance","created":"2025-05-16","cited_by_count":1,"area":null},{"id":"2506.19360","title":"SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2508.09550","title":"Exploring the Equivalence of Closed-Set Generative and Real Data Augmentation in Image Classification","created":"2025-08-13","cited_by_count":1,"area":null},{"id":"2311.15648","title":"Reinforcement Learning from Diffusion Feedback: Q* for Image Search","created":"2023-11-27","cited_by_count":0,"area":"mllm"},{"id":"2404.03263","title":"On the Surprising Efficacy of Distillation as an Alternative to Pre-Training Small Models","created":"2024-04-04","cited_by_count":0,"area":"distill"},{"id":"2406.05432","title":"Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language Models","created":"2024-06-08","cited_by_count":0,"area":"mllm"},{"id":"2501.13718","title":"A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation","created":"2025-01-23","cited_by_count":0,"area":null},{"id":"2506.09955","title":"Canonical Latent Representations in Conditional Diffusion Models","created":"2025-06-11","cited_by_count":0,"area":"diffusion"},{"id":"2510.02627","title":"A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2510.15194","title":"Salient Concept-Aware Generative Data Augmentation","created":"2025-10-16","cited_by_count":0,"area":null},{"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":"2604.09304","title":"GeRM: A Generative Rendering Model From Physically Realistic to Photorealistic","created":"2026-04-10","cited_by_count":0,"area":null},{"id":"2607.23835","title":"Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations","created":"2026-07-26","cited_by_count":0,"area":null}],"next_cursor":null}