Searching Towards Class-Aware Generators for Conditional Generative Adversarial Networks

Conditional generative adversarial networks (cGANs) are designed to generate images based on the provided conditions, e.g ., class-level distributions, semantic label maps, etc . Existing methods have used the same generator architecture for all classes. This paper presents an idea that adopts neura...

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Vydané v:	IEEE signal processing letters Ročník 29; s. 1669 - 1673
Hlavní autori:	Zhou, Peng, Xie, Lingxi, Ni, Bingbing, Tian, Qi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	class-aware Computer architecture Conditional generative adversarial networks Convolution Generative adversarial networks Generators Markov processes Microprocessors neural architecture search Optimization Search algorithms Training Training data
ISSN:	1070-9908, 1558-2361
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