PHOTOREALISTIC IMAGE SYNTHESIS FROM TEXTDESCRIPTION USING MACHINE LEARNING

Project batch members:
N. M. Hariharan (15DC05)
R. Jeevananthan (15DC06)
S. Praneet (15DC17)
P. Yathinder (15DC27)

Project Guide:
Mrs. T. P. Kamatchi

PROBLEM DEFINITION
Photorealistic image synthesis has been difficult till date with the existing technologies however Artificial Intelligence makes this possible,
we can benefit a lot from the wide application of this emerging technology. It can be employed to replace human labors in completing manytedious tasks. The objective of this project is to generate photorealistic images from a text description given by a user of a specific thing,object or being using machine learning. This project can be used by designers, engineers to generate designs based on concepts that they’vethought.

OBJECTIVES OF THIS PROJECT➤ To generate realistic images based of concepts and ideas

based of the images used to train the model➤ To make the machine learning model generic, adaptable tobe trained on different datasets.

OVERVIEW OF THE MODEL
We propose a system using a Generative Adversarial Network (GAN) which is a machine learning model which consists of a Generator and aDiscriminator both of which are trained with the same dataset, the Generator is used to generate fake images based of the real images fromthe dataset that’s used to train it. The Discriminator classifies the generated image as fake or real, when the Generator generates an image socompelling and realistic the Discriminator classifies it as real.

OVERVIEW
This project is built using a stack GAN which consists of two GANs, the first GAN is called a stage 1 GAN which takes the sentences which arerepresented as word vectors and generates an image with primitive shapes and basic colors, it is a low resolution image, the second GAN isa stage 2 GAN which takes the image generated by the stage 1 GAN and the original text description as the input and generates a much higher
resolution version of the image by completing the details.

Stack Gan Workflow diagram

Input - text description

Stage 1 GANGenerator

Stage 2 GAN

Discriminator

Generator

Discriminator

DEPENDENCIES FOR THE PROJECT
Software dependencies➤ Python programming language➤ Tensorflow - Open source machinelearning library

Hardware dependencies➤ A PC with a powerful 64-bit multicore processor and at least 4gigabytes of RAM.

PYTHON
Python though slower supports a wide range of libraries and easier tofind and install dependencies for python than other programming
languages capable of ML like “Go” from Google and Python is open
source and has good community support making it easier for debugging

TENSORS
A tensor is a mathematical object analogous to but more general than a vector, represented by an array of components that arefunctions of the coordinates of a space.

TENSORFLOW
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in
the graph represent mathematical operations, while the graph edges represent the multidimensional dataarrays (tensors) communicated between them. The flexible architecture allows you to deploy computation
to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow wasoriginally developed by researchers and engineers working on the Google Brain Team within Google'sMachine Intelligence research organization for the purposes of conducting machine learning and deepneural networks research, but the system is general enough to be applicable in a wide variety of otherdomains as well.

TIMELINE
Duration

ModulesJuly

Learning Python, Machine Learning / Chapter 1 Introduction

AugustLearning Tensorflow / Chapter 2 - Literature survey,
Chapter 3 - System study and analysis

SeptemberCollection of the training and testing dataset and beginning of thedevelopment phase of the sGAN machine learning model / Chapter 4 System development, Chapter 5 - System implementation

OctoberDeveloping the sGAN machine learning model /Chapter 6 - Testing and experimental result

REFERENCES➤ OPEN AI➤ Google Deepmind➤ Tensorflow➤ Google Magenta

“With artificial intelligence we are summoning thedemon. In all those stories where there’s the guy
with the pentagram and the holy water, it’s like –yeah, he’s sure he can control the demon.
Doesn’t work out.
-Elon Musk