{"total":31,"rows":[{"id":"2308.07863","title":"StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models","created":"2023-08-15","cited_by_count":155,"area":"diffusion"},{"id":"2310.09336","title":"Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task","created":"2023-10-13","cited_by_count":93,"area":"diffusion"},{"id":"2311.17901","title":"SODA: Bottleneck Diffusion Models for Representation Learning","created":"2023-11-29","cited_by_count":77,"area":"diffusion"},{"id":"2403.18818","title":"ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion","created":"2024-03-27","cited_by_count":71,"area":"diffusion"},{"id":"2306.08757","title":"InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models","created":"2023-06-14","cited_by_count":50,"area":"diffusion"},{"id":"2301.13721","title":"DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models","created":"2023-01-31","cited_by_count":30,"area":"diffusion"},{"id":"2402.09712","title":"Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement","created":"2024-02-15","cited_by_count":27,"area":"diffusion"},{"id":"2307.08283","title":"Complexity Matters: Rethinking the Latent Space for Generative Modeling","created":"2023-07-17","cited_by_count":19,"area":"diffusion"},{"id":"2502.04549","title":"Mechanisms of Projective Composition of Diffusion Models","created":"2025-02-06","cited_by_count":14,"area":"diffusion"},{"id":"2305.17489","title":"Text-to-image Editing by Image Information Removal","created":"2023-05-27","cited_by_count":12,"area":"diffusion"},{"id":"2506.07883","title":"Diffusion Counterfactual Generation with Semantic Abduction","created":"2025-06-09","cited_by_count":10,"area":"diffusion"},{"id":"2402.02346","title":"Closed-Loop Unsupervised Representation Disentanglement with $\\beta$-VAE Distillation and Diffusion Probabilistic Feedback","created":"2024-02-04","cited_by_count":9,"area":"diffusion"},{"id":"2210.11058","title":"Representation Learning with Diffusion Models","created":"2022-10-20","cited_by_count":6,"area":"diffusion"},{"id":"2410.23820","title":"Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models","created":"2024-10-31","cited_by_count":4,"area":"diffusion"},{"id":"2511.00103","title":"FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Video","created":"2025-10-30","cited_by_count":4,"area":"diffusion"},{"id":"2504.00220","title":"Can Diffusion Models Disentangle? A Theoretical Perspective","created":"2025-03-31","cited_by_count":3,"area":"diffusion"},{"id":"2504.14450","title":"Causal Disentanglement for Robust Long-tail Medical Image Generation","created":"2025-04-20","cited_by_count":3,"area":"diffusion"},{"id":"2509.16447","title":"Local Mechanisms of Compositional Generalization in Conditional Diffusion","created":"2025-09-19","cited_by_count":3,"area":"diffusion"},{"id":"2601.22057","title":"Unsupervised Decomposition and Recombination with Discriminator-Driven Diffusion Models","created":"2026-01-29","cited_by_count":3,"area":"diffusion"},{"id":"2602.05214","title":"Disentangled Representation Learning via Flow Matching","created":"2026-02-05","cited_by_count":2,"area":"diffusion"},{"id":"2602.17901","title":"MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis","created":"2026-02-19","cited_by_count":1,"area":"diffusion"},{"id":"2606.06120","title":"Diff-CA: Separating Common and Salient Factors with Diffusion Models","created":"2026-06-04","cited_by_count":1,"area":"diffusion"},{"id":"2410.20158","title":"Your Image is Secretly the Last Frame of a Pseudo Video","created":"2024-10-26","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":"2510.02182","title":"Uncovering Semantic Selectivity of Latent Groups in Higher Visual Cortex with Mutual Information-Guided Diffusion","created":"2025-10-02","cited_by_count":0,"area":"diffusion"},{"id":"2510.21210","title":"On the flow matching interpretability","created":"2025-10-24","cited_by_count":0,"area":"diffusion"},{"id":"2512.17127","title":"Disentangled representations via score-based variational autoencoders","created":"2025-12-18","cited_by_count":0,"area":"diffusion"},{"id":"2605.07676","title":"Structured Coupling for Flow Matching","created":"2026-05-08","cited_by_count":0,"area":"diffusion"},{"id":"2607.12404","title":"Contrastive-Augmented Flow Matching for Style-Content Disentanglement","created":"2026-07-14","cited_by_count":0,"area":"diffusion"},{"id":"2607.26304","title":"MoSAIC: Aligned Intervention Supervision for Part-Local Motion Style Transfer","created":"2026-07-28","cited_by_count":0,"area":"diffusion"},{"id":"2609.35106","title":"DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction","created":"2026-09-28","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}