Support Matching: A Novel Regularization to Escape from Mode Collapse in GANs

Yao, Yinghua; Pan, Yuangang; Tsang, Ivor W.; Yao, Xin

doi:10.1007/978-3-030-36808-1_5

Cited by 1 publication

(2 citation statements)

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“…The authors used a normalized Gram matrix to measure the similarity of the generated image features. Likewise, Yao et al (2019) proposed support regularized-GAN (SR-GAN) to improve the diversity of the generated data and eliminate the mode collapse issue. The proposed model is based on forcing the generator to cover all sub-structures of the data support and the support of the generated data and the real data, where the support of the data represents the data space with a probability density larger than zero.…”

Section: Mode Collapsementioning

confidence: 99%

“…Approaches focused on optimizing the objective function (Abusitta et al, 2021; Arjovsky et al, 2017; L. Cai, Chen, et al, 2020; Gnanha et al, 2022; Huang et al, 2021; Karnewar & Wang, 2020; Murray & Rawat, 2022; Pei et al, 2021; Shao et al, 2020; Tao & Wang, 2020; N.‐T. Tran, Bui, & Cheung, 2019; Yao et al, 2019; Zadorozhnyy et al, 2021; Z. Zhou, Zhong, et al, 2020). …”

Section: Gan Challengesmentioning

confidence: 99%

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A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Mohammed

2023

WIREs Computational Stats

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In machine learning, a generative model is responsible for generating new samples of data in terms of a probabilistic model. Generative adversarial network (GAN) has been widely used to generate realistic samples in different domains and outperforms its peers in the generative models family. However, producing a robust GAN model is not a trivial task because many challenges face the GAN during the training process and impact its performance, affecting the quality and diversity of the generated samples. In this article, we conduct a comprehensive review of GANs to present the fundamentals of GAN, including its components, types, and objective functions. Also, we present an overview of the evaluation matrices used to evaluate GAN models. Moreover, we list the applications of GANs and research work in various domains. Finally, we present the challenges that face GANs and highlight two significant issues, representing mode collapse and training instability, in addition to those research efforts that tackle these challenges.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks

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Section: Mode Collapsementioning

confidence: 99%