Date: Thu, 28 Mar 2024 09:04:17 +0000 (UTC)
Message-ID: <1180231410.966.1711616657432@759870d846b0>
Subject: Exported From Confluence
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Contributors
- renukananda td
- Hemashree R
- Vidyashree K S
- Girish L
Project Overview:
The Main Objective :
Since there is a lack of open datasets in the telecom domain, we don't o=
btain large datasets especially for performing machine learning projects. H=
ence we are aiming to deliver synthetic data to the virtual domain so that =
the researchers and also telecom companies can use this synthetic data for =
their experiments.
Why there's a need to Generate=
Synthetic Data?
In the world of information techno=
logy, companies use data to improve the customer experience and provide bet=
ter services to their customers. Sometimes, the collection of data can be t=
edious and costly.
Currently, GANs are popularly appl=
ied to generate image data, but very few works are done on the tabular data=
. One of the reasons is non-image synthetic data is difficult to evaluate q=
uality. In this work, we are trying to generate synthetic data from sc=
ratch.
GANs:
GANs stands for Generative Adversarial Networks. =E2=80=
=9CGANS=E2=80=9D well it might sound complex but actually it's not. Ian Good Fellow et al. published =E2=80=9CGenerative Adver=
sarial Networks=E2=80=9D in 2014, which was the first study to describe GAN=
s. Generative adversarial networks (GANs) are an exciting recent innovation=
in machine learning. GANs are generative models: they create new=
data instances that resemble your training data.
The GAN architecture consists of t=
wo components called Generator and Discriminator=
strong>. The role of the generator is to generate new data (numbers, =
images, etc.) which is as close/similar to the dataset that is provided as =
input, and the role of the discriminator is to differ between generated dat=
a and real input data. Since then, GANs have seen a lot of attention=
given that they are perhaps one of the most effective techniques for gener=
ating synthetic data.
A=
pproach:
In order to generate synthetic data, we are considering deep learning te=
chniques such as GANs.
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