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www.v7labs.com | ||
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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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distill.pub
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| | | | | What we'd like to find out about GANs that we don't know yet. | |
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neptune.ai
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| | | | | The generative models method is a type of unsupervised learning. In supervised learning, the deep learning model learns to map the input to the output. In each iteration, the loss is being calculated and the model is optimised using backpropagation. In unsupervised learning, we don't feed the target variables to the deep learning model like... | |
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www.livescience.com
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| | | Two new AI models allow robots to perform complex, multistep tasks in a way that they couldn't previously. | ||
