Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification

doi:doi:10.57702/2g0rb1tf

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Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification

Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature sequence datasets.

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Stephanie Ger1, Diego Klabjan2 (2024). Dataset: Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification. https://doi.org/10.57702/2g0rb1tf

DOI retrieved: December 3, 2024

Additional Info

Field	Value
Created	December 3, 2024
Last update	December 3, 2024
Defined In	https://doi.org/10.48550/arXiv.1901.02514
Author	Stephanie Ger1
More Authors	Diego Klabjan2
Homepage	https://github.com/code-submission/