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neptune.ai | ||
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www.v7labs.com
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| | | | | What are Generative Adversarial Networks and how do they work? Learn about GANs architecture and model training, and explore the most popular generative models variants and their limitations. | |
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blog.otoro.net
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| | | | | [AI summary] This text discusses the development of a system for generating large images from latent vectors, combining Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). It explores the use of Conditional Perceptual Neural Networks (CPPNs) to create images with specific characteristics, such as style and orientation, by manipulating latent vectors. The text also covers the ability to perform arithmetic on latent vectors to generate new images and the potential for creating animations by transitioning between different latent states. The author suggests future research directions, including training on more complex datasets and exploring alternative training objectives beyond Maximum Likelihood. | |
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www.analyticsvidhya.com
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| | | | | DCGAN uses convolutional and convolutional-transpose layers in the generator and discriminator, respectively for image data | |
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sander.ai
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| | | Diffusion models have become very popular over the last two years. There is an underappreciated link between diffusion models and autoencoders. | ||
