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• W3147476671 abstract "Generalized zero-shot learning (GZSL) aims to recognize samples from all classes based on training samples of seen classes by bridging the gap between the seen and unseen classes through the semantic descriptions (attributes). Recently, generative-based methods have been used to convert the GZSL task into a supervised learning problem by generating visual features for unseen classes. In this paper, we propose a dual framework based on variational auto-encoder (VAE) and generative adversarial network (GAN), known as dual VAEGAN, to produce more clear visual features than VAE and alleviate the model collapse problem of GAN. To avoid generating unconstraint visual features, the generated visual features are forced to map back into their respective common space using a cycle consistency loss. Meanwhile, the cycle-consistency loss promotes the diversity and preserves the semantic consistency of the generated visual features. The experimental results on the six standard datasets indicate that dual VAEGAN can produce promising results as compared with other methods reported in the literature." @default.
• W3147476671 created "2021-04-13" @default.
• W3147476671 creator A5031691031 @default.
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• W3147476671 date "2021-08-01" @default.
• W3147476671 modified "2023-09-30" @default.
• W3147476671 title "Dual VAEGAN: A generative model for generalized zero-shot learning" @default.
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• W3147476671 doi "https://doi.org/10.1016/j.asoc.2021.107352" @default.
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