Enhancing GANs With MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition

Enhancing GANs With MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition

Generative Adversarial Networks (GANs) have gained considerable attention owing to their impressive ability to generate high-quality, realistic images from a desired data distribution. This research introduces advancements in GANs by developing an improved activation function, a novel training strat...

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Bibliographic Details
Main Authors: Prasanna Reddy Pulakurthi, Mahsa Mozaffari, Sohail A. Dianat, Jamison Heard, Raghuveer M. Rao, Majid Rabbani
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Activation function
generative adversarial network
maximum mean discrepancy
neural architecture search
tensor decomposition
Online Access:https://ieeexplore.ieee.org/document/10732016/
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