{"total":21,"rows":[{"id":"2304.14530","title":"Generating images of rare concepts using pre-trained diffusion models","created":"2023-04-27","cited_by_count":79,"area":"diffusion"},{"id":"2306.08687","title":"Norm-guided latent space exploration for text-to-image generation","created":"2023-06-14","cited_by_count":43,"area":"diffusion"},{"id":"2307.16424","title":"MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning","created":"2023-07-31","cited_by_count":40,"area":"diffusion"},{"id":"2205.15463","title":"Few-Shot Diffusion Models","created":"2022-05-30","cited_by_count":38,"area":"diffusion"},{"id":"2502.05743","title":"Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling","created":"2025-02-09","cited_by_count":18,"area":"diffusion"},{"id":"2306.14770","title":"ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion","created":"2023-06-26","cited_by_count":16,"area":"diffusion"},{"id":"2311.00618","title":"De-Diffusion Makes Text a Strong Cross-Modal Interface","created":"2023-11-01","cited_by_count":14,"area":"diffusion"},{"id":"2210.13774","title":"From Points to Functions: Infinite-dimensional Representations in Diffusion Models","created":"2022-10-25","cited_by_count":11,"area":"diffusion"},{"id":"2212.05404","title":"Cap2Aug: Caption guided Image to Image data Augmentation","created":"2022-12-11","cited_by_count":10,"area":"diffusion"},{"id":"2405.14132","title":"Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalization","created":"2024-05-23","cited_by_count":10,"area":"diffusion"},{"id":"2312.02548","title":"GeNIe: Generative Hard Negative Images Through Diffusion","created":"2023-12-05","cited_by_count":8,"area":"diffusion"},{"id":"2312.12227","title":"HuTuMotion: Human-Tuned Navigation of Latent Motion Diffusion Models with Minimal Feedback","created":"2023-12-19","cited_by_count":6,"area":"diffusion"},{"id":"2305.08092","title":"Meta-DM: Applications of Diffusion Models on Few-Shot Learning","created":"2023-05-14","cited_by_count":4,"area":"diffusion"},{"id":"2406.06079","title":"Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks","created":"2024-06-10","cited_by_count":3,"area":"diffusion"},{"id":"2503.19371","title":"Flow to Learn: Flow Matching on Neural Network Parameters","created":"2025-03-25","cited_by_count":3,"area":"diffusion"},{"id":"2404.16556","title":"Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models","created":"2024-04-25","cited_by_count":1,"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":"2510.03608","title":"Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL","created":"2025-10-04","cited_by_count":0,"area":"diffusion"},{"id":"2512.23210","title":"Task-oriented Learnable Diffusion Timesteps for Universal Few-shot Learning of Dense Tasks","created":"2025-12-29","cited_by_count":0,"area":"diffusion"},{"id":"2603.22241","title":"MemDLM: Memory-Enhanced DLM Training","created":"2026-03-23","cited_by_count":0,"area":"diffusion"},{"id":"2605.08557","title":"MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching","created":"2026-05-08","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}