GAN Training

GAN training aims to stabilize and improve the performance of Generative Adversarial Networks, which generate realistic data by pitting a generator against a discriminator in a minimax game. Current research focuses on novel loss functions, regularization techniques (e.g., Lipschitz continuity constraints, score matching), and architectural modifications to address issues like mode collapse, overfitting (especially with limited data), and training instability. These advancements are significant because they enhance the quality and diversity of generated data, impacting diverse fields such as medical imaging, video generation, and image editing, where high-fidelity synthetic data is crucial for various applications.

Papers

November 6, 2024

ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks
Ziji Shi, Jialin Li, Yang You
Generative Adversarial Network GAN Model Adversarial Learning Generative Question GAN Training Asynchronous Training

September 30, 2024

Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization
Osama Mustafa
Generative Adversarial Network Generative AI GAN Model GAN Training Online Optimization

September 17, 2024

OSV: One Step is Enough for High-Quality Image to Video Generation
Xiaofeng Mao, Zhengkai Jiang, Fu-Yun Wang, Wenbing Zhu, Jiangning Zhang, Hao Chen, Mingmin Chi, Yabiao Wang
Video Generation Video Diffusion Model Cross Over Step GAN Training High Quality Image

August 20, 2024

MS$^3$D: A RG Flow-Based Regularization for GAN Training with Limited Data
Jian Wang, Xin Lan, Yuxin Tian, Jiancheng Lv
Generative Adversarial Network Adversarial Learning Regularization Model Limited Data GAN Training Renormalization Group Novel Regularization

August 10, 2024

Generative Adversarial Networks for Solving Hand-Eye Calibration without Data Correspondence
Ilkwon Hong, Junhyoung Ha
Generative Model Generative Adversarial Network GAN Training Hand Eye Calibration

July 16, 2024

Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images Denoising
Luca Tirel, Ali Mohamed Ali, Hashim A. Hashim
Generative Adversarial Network Image Denoising GAN Training Wasserstein Gan Gradient Penalty

June 7, 2024

GANetic Loss for Generative Adversarial Networks with a Focus on Medical Applications
Shakhnaz Akhmedova, Nils Körber
Generative Adversarial Network Loss Function Human Driving Focus GAN Training Medical Application GANetic Loss

May 27, 2024

MCGAN: Enhancing GAN Training with Regression-Based Generator Loss
Baoren Xiao, Hao Ni, Weixin Yang
Generative Adversarial Network Loss Function GAN Model GAN Training State of the Art GAN

March 31, 2024

CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization
Yao Ni, Piotr Koniusz
Generative Adversarial Network Strong Generalization Batch Normalization Normalization Dictionary GAN Training Firth Bias Reduction Lipschitz Continuity

March 27, 2024

Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks
Srinitish Srinivasan, Varenya Pathak
Generative Adversarial Network Image Generation GAN Training Color Object Abstract Art

March 7, 2024

StableDrag: Stable Dragging for Point-based Image Editing
Yutao Cui, Xiaotong Zhao, Guozhen Zhang, Shengming Cao, Kai Ma, Limin Wang
GAN Training Long Range Interaction Point Based Image Editing

November 30, 2023

SMaRt: Improving GANs with Score Matching Regularity
Mengfei Xia, Yujun Shen, Ceyuan Yang, Ran Yi, Wenping Wang, Yong-jin Liu
Generative Adversarial Network Adversarial Loss GAN Training Score Matching

November 2, 2023

Time-series Generation by Contrastive Imitation
Daniel Jarrett, Ioana Bica, Mihaela van der Schaar
Autoregressive Model GAN Training Generative Framework Time Series Generation Contrastive Imitation

November 1, 2023

Flooding Regularization for Stable Training of Generative Adversarial Networks
Iu Yahiro, Takashi Ishida, Naoto Yokoya
Generative Adversarial Network Adversarial Loss GAN Training Stable Training

October 27, 2023

Addressing GAN Training Instabilities via Tunable Classification Losses
Monica Welfert, Gowtham R. Kurri, Kyle Otstot, Lalitha Sankar
GAN Training GAN Loss F GAN

October 25, 2023

Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs
Jianwei Fei, Zhihua Xia, Benedetta Tondi, Mauro Barni
GAN Model GAN Training Ownership Verification Blind Watermarking Watermarking Loss

October 18, 2023

On Unsupervised Image-to-image translation and GAN stability
BahaaEddin AlAila, Zahra Jandaghi, Abolfazl Farahani, Mohammad Ziad Al-Saad
Generative Adversarial Network Image to Image Translation GAN Training Unsupervised Image to Image Translation

October 6, 2023

DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion
Dongjie Chen, Sen-ching S. Cheung, Chen-Nee Chuah
Private Data GAN Training Model Inversion

July 25, 2023

CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets
Mominul Islam, Hasib Zunair, Nabeel Mohammed
Generative Adversarial Network GAN Model Medical Image Datasets GAN Training Image Filtering Class Variability

June 26, 2023

A Simple and Effective Baseline for Attentional Generative Adversarial Networks
Mingyu Jin, Chong Zhang, Qinkai Yu, Haochen Xue, Xiaobo Jin, Xi Yang
Generative Adversarial Network GAN Model C Gan GAN Training Effective Baseline