GAN-MVAE: A discriminative latent feature generation framework for generalized zero-shot learning

•Propose a deep generative model (called GAN-MVAE) for Generalized Zero-Shot Learning.•Align real and generated feature distributions in the latent space of MVAE.•Propose a novel MVAE to preserve multi-modal information of the class in the latent space.•Provide some inspiration for the study of mult...

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Vydané v:	Pattern recognition letters Ročník 155; s. 77 - 83
Hlavní autori:	Ma, Peirong, Lu, Hong, Yang, Bohong, Ran, Wu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.03.2022 Elsevier Science Ltd
Predmet:	Cross-modal reconstruction Distribution alignment Embedding Generalized Zero-Shot Learning (GZSL) Generative adversarial Network (GAN) Generative adversarial networks Reconstruction Semantics Training Variational Autoencoder (VAE) Zero-shot learning Cross-modal reconstruction Generative adversarial Network (GAN) Variational Autoencoder (VAE) Distribution alignment Generalized Zero-Shot Learning (GZSL)
ISSN:	0167-8655, 1872-7344
On-line prístup:	Získať plný text
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