From Noise to Nuance: Advances in Deep Generative Image Models
Georgia Institute of Technology（佐治亚理工学院）；AppCubic（AppCubic公司）；JTB Technology Corp.（JTB科技公司）；Indiana University（印第安纳大学）；Purdue University（普渡大学）；The University of Texas at Dallas（德州大学达拉斯分校）；University of Liverpool（利物浦大学）；Emory University（埃默里大学）；National Taiwan Normal University（台湾师范大学）
Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing architectural innovations and empirical results, this paper analyzes the transition from traditional generative methods to advanced architectures, with focus on compute-efficient diffusion models and vision transformer architectures. We examine how recent developments in Stable Diffusion, DALL-E, and consistency models have redefined the capabilities and performance boundaries of image synthesis, while addressing persistent challenges in efficiency and quality. Our analysis focuses on the evolution of latent space representations, cross-attention mechanisms, and parameter-efficient training methodologies that enable accelerated inference under resource constraints. While more efficient training methods enable faster inference, advanced control mechanisms like ControlNet and regional attention systems have simultaneously improved generation precision and content customization. We investigate how enhanced multi-modal understanding and zero-shot generation capabilities are reshaping practical applications across industries. Our analysis demonstrates that despite remarkable advances in generation quality and computational efficiency, critical challenges remain in developing resource-conscious architectures and interpretable generation systems for industrial applications. The paper concludes by mapping promising research directions, including neural architecture optimization and explainable generation frameworks.
Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications
Florida International University（佛罗里达国际大学）；Manipal Institute of Technology（曼海姆技术学院）；University of Southern California（南加州大学）；Indian Statistical Institute（印度统计研究所）；University of Wollongong（沃林根大学）
Recent Advances for Quantum Neural Networks in Generative Learning
Defense Innovation Institute（国防创新研究院）；Tianjin Artificial Intelligence Innovation Center（天津人工智能创新中心）；Imperial College London（帝国理工学院）；JD Explore Academy（京东探索研究院）；National University of Defense Technology（国防科技大学）；The University of Sydney（悉尼大学）；Hon Hai (Foxconn) Research Institute（鸿海（富士康）研究院）
On the Importance of Behavioral Nuances: Amplifying Non-Obvious Motor Noise Under True Empirical Considerations May Lead to Briefer Assays and Faster Classification Processes
Rutgers the State University of New Jersey（新泽西罗切斯特州立大学）；Rutgers University Center for Cognitive Science（罗切斯特大学认知科学中心）；Rutgers University Center Biomedicine, Imaging and Modeling（罗切斯特大学生物医学、成像与建模中心）
LAD-Drive: Bridging Language and Trajectory with Action-Aware Diffusion Transformers
Institute for Intelligent Systems, Esslingen University of Applied Sciences（智能系统研究所，埃斯林根应用科学大学）；Department of Computer Science, University of Freiburg（计算机科学系，弗赖堡大学）
Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy
Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Massachusetts Institute of Technology（麻省理工学院）；University of Chicago（芝加哥大学）；University of California Santa Barbara（加州大学圣巴巴拉分校）
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
University of California Santa Barbara（加州大学圣巴巴拉分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Cancer Center at Illinois（伊利诺伊癌症中心）
