Archive | Generative Adversarial Networks

A Gentle Introduction to Upsampling and Transpose Convolution Layers for Generative Adversarial Networks

A Gentle Introduction to Upsampling and Transpose Convolution Layers for GANs

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The GAN architecture is comprised of both a generator and a discriminator model. The generator is responsible for creating new outputs, such as images, that plausibly could have come from the original dataset. The […]

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How to Implement Hacks to Train Stable Generative Adversarial Networks

How to Implement GAN Hacks to Train Stable Generative Adversarial Networks

Generative Adversarial Networks, or GANs, are challenging to train. This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means that improvements to one model come at the cost of a degrading of performance in the other model. The result is a very unstable training […]

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How to Train Stable Generative Adversarial Networks

Tips for Training Stable Generative Adversarial Networks

The Empirical Heuristics, Tips, and Tricks That You Need to Know to Train Stable Generative Adversarial Networks (GANs). Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods such as deep convolutional neural networks. Although the results generated by GANs can be remarkable, it can be challenging to […]

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A Gentle Introduction to General Adversarial Networks (GANs)

A Gentle Introduction to Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks. Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used […]

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Example of GAN Generated Images with Super Resolution

18 Impressive Applications of Generative Adversarial Networks (GANs)

A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling. Generative modeling involves using a model to generate new examples that plausibly come from an existing distribution of samples, such as generating new photographs that are similar but specifically different from a dataset of existing photographs. A GAN is […]

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Example of Photorealistic Human Faces Generated by a GAN

Best Resources for Getting Started With Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling. GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo colorization, face de-aging, super-resolution, and more. It can be very challenging to get started with GANs. This is […]

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