Generative Models over Neural Controllers for Transfer Learning

Butterworth, James; Savani, Rahul; Tuyls, Karl

doi:10.1007/978-3-031-14714-2_28

Cited by 1 publication

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“…Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) are prominent among these models, which have brought about revolutionary changes in data generation and representation learning [2]. VAEs, developed by Kingma and Welling, are lauded for their robust probabilistic frameworks and efficient latent representation learning capabilities, offering substantial benefits across a broad spectrum of machine learning applications [3]. Meanwhile, GANs, introduced by Goodfellow et al, have set new standards in the field with their ability to generate exceptionally realistic and detailed images, fundamentally transforming image synthesis [1].…”

Section: Introductionmentioning

confidence: 99%

Enhancing capabilities of generative models through VAE-GAN integration: A review

Cai

2024

ACE

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Our review explores the integration of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), which are pivotal in the realm of generative models. VAEs are renowned for their robust probabilistic foundations and capacity for complex data representation learning, while GANs are celebrated for generating high-fidelity images. Despite their strengths, both models have limitations: VAEs often produce less sharp outputs, and GANs face challenges with training stability. The hybrid VAE-GAN models harness the strengths of both architectures to overcome these limitations, enhancing output quality and diversity. We provide a comprehensive overview of VAEs and GANs technology developments, their integration strategies, and resultant performance improvements. Applications across various fields, such as artistic creation, medical imaging, e-commerce, and video gaming, highlight the transformative potential of these models. However, challenges in model robustness, ethical concerns, and computational demands persist, posing significant hurdles. Future research directions are poised to transform the VAE-GAN landscape significantly. Enhancing training stability remains a priority, with new approaches such as incorporating self-correcting mechanisms into GANs training being tested. Addressing ethical issues is also critical, as policymakers and technologists work together to develop standards that prevent misuse. Moreover, reducing computational costs is fundamental to democratizing access to these technologies. Projects such as the development of MobileNetV2 have made strides in creating more efficient neural network architectures that maintain performance while being less resource-intensive. Further, the exploration of VAE-GAN applications in fields like augmented reality and personalized medicine offers exciting opportunities for growth, as evidenced by recent pilot studies.

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Section: Introductionmentioning

confidence: 99%

Enhancing capabilities of generative models through VAE-GAN integration: A review

Cai

2024

ACE

View full text Add to dashboard Cite

show abstract

Generative Models over Neural Controllers for Transfer Learning

Cited by 1 publication

References 10 publications

Enhancing capabilities of generative models through VAE-GAN integration: A review

Enhancing capabilities of generative models through VAE-GAN integration: A review

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