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.
Papers
Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models
Fengzhe Zhang, Jiajun He, Laurence I. Midgley, Javier Antorán, José Miguel Hernández-Lobato
Exploring User-level Gradient Inversion with a Diffusion Prior
Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley Malin, Kieran Parsons, Ye Wang
Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models
Sanoojan Baliah, Qinliang Lin, Shengcai Liao, Xiaodan Liang, Muhammad Haris Khan
Alignment of Diffusion Models: Fundamentals, Challenges, and Future
Buhua Liu, Shitong Shao, Bao Li, Lichen Bai, Haoyi Xiong, James Kwok, Sumi Helal, Zeke Xie
Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning
Yingling Lu, Yijun Yang, Zhaohu Xing, Qiong Wang, Lei Zhu
Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models
Jiahang Cao, Qiang Zhang, Jingkai Sun, Jiaxu Wang, Hao Cheng, Yulin Li, Jun Ma, Yecheng Shao, Wen Zhao, Gang Han, Yijie Guo, Renjing Xu
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models
Boming Miao, Chunxiao Li, Yao Zhu, Weixiang Sun, Zizhe Wang, Xiaoyi Wang, Chuanlong Xie
SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation
Teng Hu, Jiangning Zhang, Ran Yi, Hongrui Huang, Yabiao Wang, Lizhuang Ma
Table-to-Text Generation with Pretrained Diffusion Models
Aleksei S. Krylov, Oleg D. Somov
Enhancing Emotional Text-to-Speech Controllability with Natural Language Guidance through Contrastive Learning and Diffusion Models
Xin Jing, Kun Zhou, Andreas Triantafyllopoulos, Björn W. Schuller
What happens to diffusion model likelihood when your model is conditional?
Mattias Cross, Anton Ragni
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning
Jiahao Lai, Jiaqi Li, Jian Xu, Yanru Wu, Boshi Tang, Siqi Chen, Yongfeng Huang, Wenbo Ding, Yang Li
Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models
Aakash Sen Sharma, Niladri Sarkar, Vikram Chundawat, Ankur A Mali, Murari Mandal
Sequential Posterior Sampling with Diffusion Models
Tristan S.W. Stevens, Oisín Nolan, Jean-Luc Robert, Ruud J.G. van Sloun
TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang