GAN-MVAE: a discriminative latent feature generation framework for generalized zero-shot learning
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Ma et al. introduced the GAN-MVAE model, which integrated GAN and VAE to align the semantic spaces of multiple modalities, enabling zero-shot learning.
“Additionally, Ma et al. introduced the GAN-MVAE model, which integrated GAN and VAE to align the semantic spaces of multiple modalities, enabling zero-shot learning [62].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [62]
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Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
“For instance, CycleGAN applied paired GANs to learn cross-image domains and facilitate translations between different modalities [ 62].”
A review on multimodal zero‐shot learning
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“Ma et al. (2022) proposed a multimodal reconstruction VAE algorithm (MVAE) for latent feature generation by combining GAN and VAE techniques.”
MethodPassalis, N., Dimitriadis, C. N., & Georgiadis, M. C. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.05.008 · Cited by 25 · see citing works
A Versatile Multimodal Learning Framework for Zero-Shot Emotion Recognition
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“Combining GAN and VAE techniques, Ma et al. [65] introduce a multimodal reconstruction VAE approach for generating latent features.”
MethodGenerating diverse augmented attributes for generalized zero shot learning
Zhao, X., Shen, Y., Wang, S., & Zhang, H. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.01.005 · Cited by 19 · see citing works
BCE4ZSR: Bi-encoder empowered by teacher cross-encoder for zero-shot cold-start news recommendation
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An Integral Projection-Based Semantic Autoencoder for Zero-Shot Learning
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A comprehensive review on zero-shot-learning techniques
Lazaros, K., Koumadorakis, D. E., Vrahatis, A. G., & Kotsiantis, S. B. (2024) · Intelligent Decision Technologies · doi:10.3233/idt-240297 · Cited by 6 · see citing works
“Ma et al. [86] introduce GAN-MVAE, a fusion of a generative adversarial network and a multi-modal variational autoencoder, paving the way for generalized zero-shot learning.”
Customization of latent space in semi-supervised Variational AutoEncoder
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Few-shot Underwater Acoustic Target Recognition Based on Siamese Network
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“Additionally, GANs have proven successful in data augmentation, although primarily in the contexts of image classification [9], [10], [11], [12] and object detection [13], [14] tasks.”
MethodBrancati, N., & Frucci, M. (2024) · Information · doi:10.3390/info15020098 · Cited by 4 · see citing works
“With the aim of preserving the spatial correlations in the WSI, some recent methods for the classification task only [11–13] and for the data augmentation improvement classification [14,15] propose mapping images from a low-level pixel space to a higher-level latent space using neural networks.”
Generative-based hybrid model with semantic representations for generalized zero-shot learning
Akdemir, E., & Barışçı, N. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03734-9 · Cited by 2 · see citing works
Sui, Z., Wang, P., Malik, H., & Akter, K. (2026) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.70361 · Cited by 2 · see citing works
A comprehensive review on zero-shot-learning techniques
Lazaros, K., Koumadorakis, D. E., Vrahatis, A. G., & Kotsiantis, S. B. (2024) · Intelligent Decision Technologies · doi:10.3233/idt-24027 · Cited by 1 · see citing works
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Zero-shot human-object interaction detection via learning from the data itself
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“17 Although traditional GAN can obtain complex data distribution, its training process is not stable.”
Contrastive Diffusion Generative Adversarial Network for Generalized Zero-Shot Learning
Zhang, Z., Qiu, K., Wang, Y., Hong, M., Li, Y., & Huang, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6596-9_19
Selecting generated synthetic features using clustering algorithm for generalized zero-shot learning
Akdemir, E., Barışçı, N., Akcayol, M. A., & Doğan, N. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01979-z
- Authors
- Ma, P., Lu, H., Yang, B., & Ran, W.
- Affiliations
- Fudan University (CN)
- Corresponding author
- Lu, Hong
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 155
- Pages
- 77-83
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W4210325695
- Cited by
- 18
- References
- 42
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2023: 5
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Pattern Recognition Letters (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- National Key Research and Development Program of China Stem Cell and Translational Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- generalized zero-shot learning, generative adversarial networks, unseen class recognition
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