GAN Model
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, addressing issues like mode collapse, and enhancing controllability over generated outputs, often through integration with other models like diffusion models or reinforcement learning. Applications span diverse fields, including image generation and editing, drug discovery, and data augmentation for tasks where real data is scarce or expensive to obtain, significantly impacting various scientific domains and practical applications. Recent work also highlights the exploration of alternative training methods to improve efficiency and quality, moving beyond traditional adversarial training.
Papers
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
Shichong Peng, Alireza Moazeni, Ke Li
OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Data
S. M Towhidul Islam Tonmoy, S. M Mehedi Zaman
Unifying conditional and unconditional semantic image synthesis with OCO-GAN
Marlène Careil, Stéphane Lathuilière, Camille Couprie, Jakob Verbeek