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

March 1, 2023

PE-GAN: Prior Embedding GAN for PXD images at Belle II
Baran Hashemi, Nikolai Hartmann, Thomas Kuhr, Martin Ritter, Matej srebre
GAN Model C Gan Particle Tracking Pixel Vertex Detector

February 28, 2023

February 27, 2023

Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network
Jingyi Gu, Fadi P. Deek, Guiling Wang
Generative Adversarial Network GAN Model Wasserstein Gan Stock Movement Prediction

February 24, 2023

RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
Shancong Mou, Xiaoyi Gu, Meng Cao, Haoping Bai, Ping Huang, Jiulong Shan, Jianjun Shi
Generative Adversarial Network GAN Model Image Restoration GAN Inversion Reference Image Pixel Wise Anomaly Detection

February 22, 2023

Uncovering Bias in Face Generation Models
Cristian Muñoz, Sara Zannone, Umar Mohammed, Adriano Koshiyama
GAN Model Bias Mitigation Group Fairness Face Generation Feature Perturbation Bias Discovery

February 21, 2023

Texturize a GAN Using a Single Image
Pengda Xiang, Sitao Xiang, Yajie Zhao
Pre Trained GAN Model Single Image Deep Generative Model Adversarial Loss Laplacian Regularization

February 18, 2023

Redes Generativas Adversarias (GAN) Fundamentos Te\'oricos y Aplicaciones
Jordi de la Torre
Generative Model Generative Adversarial Network Image Generation GAN Model Adversarial Learning Generative Question

February 16, 2023

Generative Adversarial Networks for Malware Detection: a Survey
Aeryn Dunmore, Julian Jang-Jaccard, Fariza Sabrina, Jin Kwak
Timely Survey Generative Adversarial Network GAN Model Generative Adversarial Malware Detection

February 9, 2023

In-N-Out: Faithful 3D GAN Inversion with Volumetric Decomposition for Face Editing
Yiran Xu, Zhixin Shu, Cameron Smith, Seoung Wug Oh, Jia-Bin Huang
Neural Radiance Field GAN Model Novel View Synthesis GAN Inversion Accurate Reconstruction Face Editing 3D GAN Inversion

February 3, 2023

Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
Bowen Tian, Qinliang Su, Jianxing Yu
Generative Adversarial Network Training Data GAN Model Adversarial Learning Generative Question Adversarial Purification

February 2, 2023

Are Diffusion Models Vulnerable to Membership Inference Attacks?
Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, Kaidi Xu
Generative Model Text to Image Diffusion Model GAN Model Latent Diffusion Model Membership Inference Attack

February 1, 2023

Towards CGAN-based Satellite Image Synthesis with Partial Pixel-Wise Annotation
Hadi Mansourifar, Steven J. Simske
GAN Model Conditional Generative Adversarial Network Pixel Level Annotation

January 31, 2023

January 30, 2023

January 22, 2023

BallGAN: 3D-aware Image Synthesis with a Spherical Background
Minjung Shin, Yunji Seo, Jeongmin Bae, Young Sun Choi, Hyunsu Kim, Hyeran Byun, Youngjung Uh
GAN Model Image Synthesis 3D Scene 3D Geometry Spherical Array

January 20, 2023