eVAE: Evolutionary Variational Autoencoder

Variational autoencoders (VAEs) are challenged by the imbalance between representation inference and task fitting caused by surrogate loss. To address this issue, existing methods adjust their balance by directly tuning their coefficients. However, these methods suffer from a tradeoff uncertainty, i...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 36; H. 2; S. 3288 - 3299
Hauptverfasser:	Wu, Zhangkai, Cao, Longbing, Qi, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2025
Schlagworte:	Convergence Deep learning Evolutionary variational autoencoder (eVAE) Fitting Probabilistic logic Task analysis Training Tuning variational autoencoder (VAE) variational genetic algorithm (VGA)
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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