Optimization of Variational Autoencoder Model for Mobile Computing Environment Using Amortized Stochastic Variational Inference and Miniaturization Techniques - Ene, D.S; Anireh, V.I.E; Matthias, D.; Bennett, E.O. (International Journal of Computer Sciences and Engineering, 2024)
Variational Autoencoders (VAEs) are powerful machine learning models that can be deployed on mobile devices. However, VAEs are often deployed on resource-constrained mobile platforms, resulting in a high computational overhead. In this study, we present a novel framework, called the Miniaturizing Variations Auto Encoder (mVAE), to overcome the computational constraints associated with VAE deployment on mobile platforms. By leveraging advanced miniaturization techniques and integrating Amortized Stochastic Variational Inference (ASVI), this framework unlocks the full potential of VAE models in the mobile realm. Through extensive experiments and performance analysis, we aim to demonstrate the feasibility and efficiency of the m VAE framework in enabling the deployment of sophisticated machine learning applications on mobile systems. The findings of this study not only contribute to the advancement of mobile computing but also pave the way for a wide range of practical applications, empowering mobile users with powerful AI capabilities. Overall, this research contributes not only to theoretical foundations but also provides practical insights into implementation, addressing the need for efficient machine learning systems in mobile computing environments.