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neptune.ai | ||
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tiao.io
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| | | | | An in-depth practical guide to variational encoders from a probabilistic perspective. | |
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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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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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torch.ch
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| | | Torch is a scientific computing framework for LuaJIT. | ||
