Regularizing Label-Augmented Generative Adversarial Networks Under Limited Data

Regularizing Label-Augmented Generative Adversarial Networks Under Limited Data

Training generative adversarial networks (GANs) using limited training data is challenging since the original discriminator is prone to overfitting. The recently proposed label augmentation technique complements categorical data augmentation approaches for discriminator, showing improved data effici...

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Bibliographic Details
Main Author:	Liang Hou
Format:	Article
Language:	English
Published:	IEEE 2023-01-01
Series:	IEEE Access
Subjects:	Generative adversarial networks limited data adaptive regularization label augmentation data augmentation self-supervised learning
Online Access:	https://ieeexplore.ieee.org/document/10076457/

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