The document discusses using generative adversarial networks (GANs) for text-to-image generation. GANs involve two neural networks, a generator and discriminator, that compete against each other. The generator generates images from text descriptions, while the discriminator tries to distinguish real images from generated ones. The document outlines the network architecture, literature review on GAN improvements, methodology used which involves training the GAN on a dataset to generate high resolution images from low resolution inputs conditioned on text.

1. TEXT TO IMAGE GENERATION USING
GENERATIVE ADVERSARIAL NETWORKS
CH AAZEEN AHMAD
01-134191-010

2. INTRODUCTION
 Text-to-image generation is a field with great potential.
 In project, we are programmatically synthesizes one data type into another, generating a photorealist
image based off a phrase.
 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 to generate or output new examples that plausibly could have been drawn from the original
dataset.
 GAN are basically two neural networks fighting against each other. It consists of two networks
Generator and Discriminator . Generator generates specific data and the analyst tries to predict the
weather data from the input database or generator

3. NETWORK ARCHITECTURE

4. LITERATURE REVIEW
Year Method / Architecture Problem Description Author
2015 DCGAN
Used deconvolutional layers in
generator and convolutional
layers in discriminator to
generate high-quality images.
Alec Radford and Luke
Metz
2018 StyleGAN
Use adaptive instance
normalization and progressive
growing to generate even higher-
quality images.
Tero Karras and Samuli
Laine
2017 WGAN
Introduced a new objective
function that improved the
stability and convergence of the
GAN training process.
Martin Arjovsky and
Soumith Chintala
Generative Adversarial Networks (GANs) have become a popular research area in deep learning since
their introduction in 2014.

5. WHY UNSUPERVISED GANS
 This is because the training process of a GAN does not require any labeled data unlike in supervised
learning algorithms.
 In supervised learning, the algorithm is trained on a labeled dataset, where the input data is paired with
the corresponding output label.
 GANs are trained to generate new data that is similar to the training data, without any explicit labels or
annotations. The training process involves two neural networks - the generator network and the
discriminator network - that compete against each other in a zero-sum game.

6. SO WHY USE GAN FOR IMAGE RESOLUTION
 GANs can generate realistic and high-quality images.
 GANs can handle complex image features, which can be challenging for other neural network
architectures.
 GANs can be combined with other neural network architectures, such as CNNs.The combination allows
GANs to generate high-resolution images from low-resolution inputs, while CNNs can be used to further
refine and enhance the generated images.
 GANs can learn from unlabeled data, which is often available in image super-resolution tasks.

7. PROJECT AIM
 This project aim is to generate a semantically consistent and visually realistic image conditioned on a
textual description
 Collecting a dataset of low-resolution face images, Preprocessing the dataset, Training a GAN model,
Evaluating the performance of the GAN model
 It is widely used in various fields of likephoto editing, art generation, and computer-aided design

8. METHODOLOGY
 During the training, A high resolution image is converted into low-resolution image and then Generative
adversarial network upgrade low resolution images to super-resolution then discriminator will distinguish
the high resolution images and backpropagate the adversarial network loss to train both the
discriminator and generator.

9. METHODOLOGY

10. SEQUENCE DIAGRAM

11. HOW TO TRAIN
Training GANs involves the following steps:
 Preparing the Dataset
 Defining the Generator and discriminator Network
 Training the GAN
 Optimizing the GAN
 Evaluating the GAN

12. TOOLS AND TECHNOLOGIES USED
Language Python 3.7
Framework and libraries Sklearn, OpenCV, Spicy, NumPy,
Pandas, Keras and Pytorch
Notebook Kaggle code and Google collab

14. SOME RESULTS FROM OUR TRAINED MODEL
This flower is pink in color with oval shaped petals

15. SOME RESULTS FROM OUR TRAINED MODEL
This flower is yellow in color with oval shaped petals

16. Thank you