Augmenting image data using generative adversarial networks (GAN)
Deep learning is proposed to employ algorithms to replace laborious human operations with more automation, improving system performance on specific tasks. The researchers have become interested in machine learning in the past decades. There are many applications for machine learning in various fi...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Thesis-Master by Coursework |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173947 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | Deep learning is proposed to employ algorithms to replace laborious human
operations with more automation, improving system performance on specific tasks.
The researchers have become interested in machine learning in the past decades.
There are many applications for machine learning in various fields. Data
augmentation is designed to create the data and improve the deep learning model
accuracy when data is rare. The generative adversarial network is a common
approach to augment the datasets.
In this report, the principles and architectures of several GAN methods including
DCGAN, conditional GAN and cycle GAN are introduced. The functions and
relationship of the generators and discriminators are discussed. By operating the
simulation in Jupiter notebook with same dataset, the influence of epoch on the
performance for the same GAN method is explored. The different simulation results
using the DCGAN and CGAN are compared in order to analyze the difference
between them. In addition, the future development of GAN is discussed. The test
accuracies comparisons between original data and augmented data are done to
analyze the function of data augmentation. |
---|