Understanding Pix2Pix GAN

he name itself says “Pixel to Pixel” which means, in an image it takes a pixel, then convert that into another pixel.

The goal of this model is to convert from one image to another image, in other words the goal is to learn the mapping from an input image to an output image.

But why and what application we can think of ??

Well, there are tons of applications we can think of:

Pix2Pix Gan

The Pix2Pix GAN has been demonstrated on a range of image-to-image translation tasks such as converting maps to satellite photographs, black and white photographs to color, and sketches of products to product photographs.

And the reason why we use GAN’s for this is to synthesize these photos from one space to another.

Pix2Pix is a Generative Adversarial Network, or GAN, model designed for general purpose image-to-image translation.

The approach was presented by Phillip Isola, et al. in their 2016 paper titled “Image-to-Image Translation with Conditional Adversarial Networks” and presented at CVPR in 2017.

Introduction to Gan

The GAN architecture is comprised of two models:

1. Generator model for outputting new plausible synthetic images, and a

2. Discriminator model that classifies images as real (from the dataset) or fake (generated).

The discriminator model is updated directly, whereas the generator model is updated via the discriminator model. As such, the two models are trained simultaneously in an adversarial process where the generator seeks to better fool the discriminator and the discriminator seeks to better identify the counterfeit images.

#tensorflow #generative-adversarial #python #deep-learning #machine-learning

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Understanding Pix2Pix GAN

6 GAN Architectures Every Data Scientist Should Know

Generative Adversarial Networks (GANs) were first introduced in 2014 by Ian Goodfellow et. al. and since then this topic itself opened up a new area of research.

Within a few years, the research community came up with plenty of papers on this topic some of which have very interesting names :). You have CycleGAN, followed by BiCycleGAN, followed by ReCycleGAN and so on.

With the invention of GANs, Generative Models had started showing promising results in generating realistic images. GANs has shown tremendous success in Computer Vision. In recent times, it started showing promising results in Audio, Text as well.

Some of the most popular GAN formulations are:

  • Transforming an image from one domain to another(CycleGAN),
  • Generating an image from a textual description (text-to-image),
  • Generating very high-resolution images (ProgressiveGAN) and many more.

In this article, we will talk about some of the most popular GAN architectures, particularly 6 architectures that you should know to have a diverse coverage on Generative Adversarial Networks (GANs).

Namely:

  • CycleGAN
  • StyleGAN
  • pixelRNN
  • text-2-image
  • DiscoGAN
  • lsGAN

#machine-learning #deep-learning #data-science #gan-algorithm #gans #gan

GANs from scratch Tutorial

Neural networks aren’t limited to just learning data; they can also learn to create it. One of the classic machine learning papers is Generative Adversarial Networks  (GANs) (2014) by Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, and others. GANs take the form of two opposing neural networks one learning to generate fake samples while the other tries to separate the real samples from the fakes.

Sophisticated GAN models like VQGAN and others generate everything from fake landscapes, faces, and even Minecraft worlds.

These are my notes on the classic paper that first introduced GANs.

#artificial-intelligence #gans #tensorflow #machine-learning #generative-adversarial #gans from scratch

Noah  Rowe

Noah Rowe

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Understanding GANs (Generative Adversarial Networks)

GANs (Generative Adversarial Networks) are a class of models where images are translated from one distribution to another. GANs are helpful in various use-cases, for example: enhancing image quality, photograph editing, image-to-image translation, clothing translation, etc. Nowadays, many retailers, fashion industries, media, etc. are making use of GANs to improve their business and relying on algorithms to do the task.

Image for post

a) Super-resolution: enhancing image quality b) MUNIT: building shoes from edges c) DeepFashion: generating guided pose with condition image

There are many forms of GAN available serving different purposes, but in this article, we will focus on CycleGAN. Here we will see its working and implementation in PyTorch. So buckle up!!

CycleGAN learns the mapping of an image from source X to a target domain Y. Assume you have an aerial image of a city and want to convert in google maps image or the landscape image into a segmented image, but you don’t have the paired images available, then there is GAN for you.

How is GAN different from Style Transfer? GAN is a more generalized model than Style Transfer. Here both methods try to solve the same problem, but the approach is different. Style transfer tries to keep the content of the image intact while applying the style of the other image. It extracts the content and style from the middle layers of the NN model. It focusses on learning the content and style of the image separately, but in GAN, the model tries to learn the entire mapping from one domain to another without segregating the learning of context and style.

GAN Architecture:

Consider two image domains, a source domain (X) and a target domain (Y). Our objective is to learn the mapping from domain G: X → Y and from F: Y → X. We have N and M training examples in domain X and Y resp.

GAN has two parts:

a) Generator (G)

The job of the Generator is to do the “translation” part. It learns the mapping from X → Y and Y → X and uses images in domain X to generate fake Y’s that look similar to the target domain and vice-versa. The design of Generators generally consists of downsampling layers followed by a series of residual blocks and upsampling layers.

b) Discriminator (D)

The job of the Discriminator is to look at an image and output whether or not it is a real training image or a fake image from the Generator. Discriminator acts like a binary “classifier” that gives the probability of the image being real. The design of the Discriminator usually consists of a series of blocks of [conv, norm, Leaky-Relu] layers. The last layer of the Discriminator outputs the matrix, which is close to one when the input image is real else close to zero. There are two discriminators (Dx and Dy) for each domain.

During training, the Generator tries to outsmart the Discriminator by generating better and better fakes. The model reaches the equilibrium when images generated by the Generator are so good that Discriminator guesses it with almost 50% confidence, whether it’s fake or real.

Loss Function:

GAN involves three types of losses:

  1. Adversarial (GAN) Loss:

Image for post

Adversarial loss

Here D(G(x)) is the probability that the output generated by G is a real image. G tries to generate the images G(x) that look similar to real image y, whereas Dy tries to distinguish between real (y) and translated (G(x)) images. D focusses on maximizing this loss function, whereas G wants to minimize this loss function, making it a minimax objective function for GAN. Similar adversarial loss follows for mapping F: Y → X.

#pytorch #machine-learning #gans #image-processing #deep-learning #deep learning

GAN | Explain the Core Concepts of GANs for Beginners

GAN is considered as one of the greatest breakthroughs in the field of Artificial Intelligence. In this video, I've tried my best to explain the core concepts of GANs.

Subscribe: https://www.youtube.com/c/NormalizedNerd/featured 

#gans  #deeplearning  #machinelearning 

Understanding Pix2Pix GAN

he name itself says “Pixel to Pixel” which means, in an image it takes a pixel, then convert that into another pixel.

The goal of this model is to convert from one image to another image, in other words the goal is to learn the mapping from an input image to an output image.

But why and what application we can think of ??

Well, there are tons of applications we can think of:

Pix2Pix Gan

The Pix2Pix GAN has been demonstrated on a range of image-to-image translation tasks such as converting maps to satellite photographs, black and white photographs to color, and sketches of products to product photographs.

And the reason why we use GAN’s for this is to synthesize these photos from one space to another.

Pix2Pix is a Generative Adversarial Network, or GAN, model designed for general purpose image-to-image translation.

The approach was presented by Phillip Isola, et al. in their 2016 paper titled “Image-to-Image Translation with Conditional Adversarial Networks” and presented at CVPR in 2017.

Introduction to Gan

The GAN architecture is comprised of two models:

1. Generator model for outputting new plausible synthetic images, and a

2. Discriminator model that classifies images as real (from the dataset) or fake (generated).

The discriminator model is updated directly, whereas the generator model is updated via the discriminator model. As such, the two models are trained simultaneously in an adversarial process where the generator seeks to better fool the discriminator and the discriminator seeks to better identify the counterfeit images.

#tensorflow #generative-adversarial #python #deep-learning #machine-learning