Diffusion Model
Diffusion models are generative models that create data by reversing a noise-diffusion process, aiming to generate high-quality samples from complex distributions. Current research focuses on improving efficiency through techniques like stochastic Runge-Kutta methods and dynamic model architectures (e.g., Dynamic Diffusion Transformer), as well as enhancing controllability and safety via methods such as classifier-free guidance and reinforcement learning from human feedback. These advancements are significantly impacting various fields, including medical imaging, robotics, and artistic creation, by enabling novel applications in image generation, inverse problem solving, and multi-modal data synthesis.
2251papers
Papers - Page 17
February 24, 2025
Dimitra: Audio-driven Diffusion model for Expressive Talking Head Generation
Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions
MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection
Mitigating Hallucinations in Diffusion Models through Adaptive Attention Modulation
Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting
February 22, 2025
February 20, 2025
DDAT: Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories
DC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
FacaDiffy: Inpainting Unseen Facade Parts Using Diffusion Models
February 19, 2025
February 18, 2025
MotionMatcher: Motion Customization of Text-to-Video Diffusion Models via Motion Feature Matching
Is Noise Conditioning Necessary for Denoising Generative Models?
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects
RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models
Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo
3D Shape-to-Image Brownian Bridge Diffusion for Brain MRI Synthesis from Cortical Surfaces
DeltaDiff: A Residual-Guided Diffusion Model for Enhanced Image Super-Resolution