Generative Adversarial Network
Generative Adversarial Networks (GANs) are a class of deep learning models designed to generate new data instances that resemble a training dataset. Current research focuses on improving GAN training stability, enhancing the quality and diversity of generated data, and applying GANs to diverse fields like medical imaging, drug discovery, and time series analysis, often incorporating techniques like contrastive learning and disentangled representation learning to improve model performance and interpretability. The ability of GANs to synthesize realistic data addresses critical limitations in data availability and annotation costs across numerous scientific disciplines and practical applications, leading to advancements in areas ranging from medical diagnosis to robotic control.
Papers
OSDFace: One-Step Diffusion Model for Face Restoration
Jingkai Wang, Jue Gong, Lin Zhang, Zheng Chen, Xing Liu, Hong Gu, Yutong Liu, Yulun Zhang, Xiaokang Yang
Addressing Vulnerabilities in AI-Image Detection: Challenges and Proposed Solutions
Justin Jiang
A generalised novel loss function for computational fluid dynamics
Zachary Cooper-Baldock, Paulo E. Santos, Russell S.A. Brinkworth, Karl Sammut
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches
Yinqiu Feng, Aoran Shen, Jiacheng Hu, Yingbin Liang, Shiru Wang, Junliang Du
Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack
Xide Xu, Muhammad Atif Butt, Sandesh Kamath, Bogdan Raducanu
Comparison of Generative Learning Methods for Turbulence Modeling
Claudia Drygala, Edmund Ross, Francesca di Mare, Hanno Gottschalk
Synthesising Handwritten Music with GANs: A Comprehensive Evaluation of CycleWGAN, ProGAN, and DCGAN
Elona Shatri, Kalikidhar Palavala, George Fazekas
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models
Donggeun Ko, Dongjun Lee, Namjun Park, Wonkyeong Shim, Jaekwang Kim