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Conditional image generation with One-Vs-All classifier

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成果类型:
期刊论文
作者:
Xu, Xiangrui;Li, Yaqin;Yuan, Cao
通讯作者:
Cao Yuan
作者机构:
[Xu, Xiangrui; Li, Yaqin; Yuan, Cao] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China.
通讯机构:
[Cao Yuan] S
School of Mathematics and Computer Science, Wuhan Polytechnic University, Hubei, China
语种:
英文
关键词:
Classifiers;Image classification;Adversarial networks;Image generations;Input datas;Jensen-Shannon divergence;Objective functions;One vs alls;Classification (of information);Article;classifier;data processing;discriminant analysis;information processing;model;priority journal;training
期刊:
Neurocomputing
ISSN:
0925-2312
年:
2021
卷:
434
页码:
261-267
基金类别:
CRediT authorship contribution statement Xiangrui Xu: Methodology, Software, Validation, Formal analysis, Writing - original draft, Writing - review & editing. Yaqin Li: Investigation, Data curation, Visualization, Supervision, acquisition. Cao Yuan: Conceptualization, Resources, Project administration.
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
摘要:
This paper explores conditional image generation with a One-Vs-All classifier based on the Generative Adversarial Networks (GANs). Instead of the real/fake discriminator used in vanilla GANs, we propose to extend the discriminator to a One-Vs-All classifier (GAN-OVA) that can distinguish each input data to its category label. Specifically, we feed certain additional information as conditions to the generator and take the discriminator as a One-Vs-All classifier to identify each conditional category. Our model can be applied to different diverge...

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