Intro to GAN's

AkulMehra

Agenda

Introduction to GAN
Architecture of GAN
Introduction to CNN
Applications of GAN's

Hello world of Machine learning

Classifying Handwritten digits using image processing and machine learning

MNIST Dataset

5 0 4 1

But with GAN's we can generate new images

Generative Adversarial Networks

Generative Adversarial Networks (GAN's) are a class of neural networks which allow a network to generate data with the same internal structure as other data.

A GAN has two parts in it:

the generator that generates images
the discriminator that classifies real and fake images

Generative Adversarial Networks

The generator trying to maximize the probability of making the discriminator mistakes its inputs as real.
And the discriminator guiding the generator to produce more realistic images.
In the perfect equilibrium, the generator would capture the general training data distribution. As a result, the discriminator would be always unsure of whether its inputs are real or not.

Structure of a GAN

The generator that generates images and the discriminator that classifies real and fake images.

Convolutional Neural Network(CNN)

Convolutional Neural Networks are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity.

How we goanna use CNN in GAN's?