Generative Adversarial Networks, or GANs, are deep learning architecture generative models that have seen wide success. There are thousands of papers on GANs and many hundreds of named-GANs, that is, models with a defined name that often includes “GAN“, such as DCGAN, as opposed to a minor extension to the method. Given the vast size […]

# Search results for "Generative Adversarial Network"

## Generative Adversarial Networks with Python

Generative Adversarial Networks with Python Deep Learning Generative Models for Image Synthesis and Image Translation …so, What are Generative Adversarial Networks? Generative Adversarial Networks, or GANs, are a deep-learning-based generative model. More generally, GANs are a model architecture for training a generative model, and it is most common to use deep learning models in this […]

## How to Develop a 1D Generative Adversarial Network From Scratch in Keras

Generative Adversarial Networks, or GANs for short, are a deep learning architecture for training powerful generator models. A generator model is capable of generating new artificial samples that plausibly could have come from an existing distribution of samples. GANs are comprised of both generator and discriminator models. The generator is responsible for generating new samples […]

## 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 […]

## 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 […]

## 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 […]

## Why is my GAN not converging?

GAN models do not converge. Instead, the generator and the discriminator models find a stable equilibrium (hopefully). The generator deceives the discriminator at some level (but not all the time) and the discriminator effectively classifies real and generated images (but not all the time). For more on this relationship between the models, see this tutorial: […]

## 14 Different Types of Learning in Machine Learning

Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence. The focus of the field is learning, that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data. As such, there are many different types of […]

## How to Calculate the KL Divergence for Machine Learning

It is often desirable to quantify the difference between probability distributions for a given random variable. This occurs frequently in machine learning, when we may be interested in calculating the difference between an actual and observed probability distribution. This can be achieved using techniques from information theory, such as the Kullback-Leibler Divergence (KL divergence), or […]

## How to Implement the Frechet Inception Distance (FID) for Evaluating GANs

The Frechet Inception Distance score, or FID for short, is a metric that calculates the distance between feature vectors calculated for real and generated images. The score summarizes how similar the two groups are in terms of statistics on computer vision features of the raw images calculated using the inception v3 model used for image […]