Upskill your Team on Generative AI. Start here >

Enhancing GAN Training Stability through Xavier Glorot Initialization: A Solution to Unstable Training

Author(s): Sourabh Mehta

Abstract

This research paper investigates the challenging task of training Generative Adversarial Networks (GANs) and addresses the issue of unstable training commonly encountered in this process. It emphasizes the significance of weight initialization in the generator and discriminator networks to stabilize GAN training. Specifically, the study focuses on Xavier Glorot initialization, a popular technique for weight initialization, and its potential to enhance the quality of generated data by promoting stability in GAN training. Through experimental analysis and evaluation, this research explores the effectiveness of Xavier Glorot initialization in achieving stable GAN training and improving the overall output quality of generated data.

Picture of Association of Data Scientists

Association of Data Scientists

The Chartered Data Scientist Designation

Achieve the highest distinction in the data science profession.

Elevate Your Team's AI Skills with our Proven Training Programs

Strengthen Critical AI Skills with Trusted Generative AI Training by Association of Data Scientists.

Our Accreditations

Get global recognition for AI skills

Chartered Data Scientist (CDS™)

The highest distinction in the data science profession. Not just earn a charter, but use it as a designation.

Certified Data Scientist - Associate Level

Global recognition of data science skills at the beginner level.

Certified Generative AI Engineer

An upskilling-linked certification initiative designed to recognize talent in generative AI and large language models

Join thousands of members and receive all benefits.

Become Our Member

We offer both Individual & Institutional Membership.