A Study on Generative Adversarial Networks Exacerbating Social Data Bias

A Study on Generative Adversarial Networks Exacerbating Social Data Bias

abstract: Generative Adversarial Networks are designed, in theory, to replicate the distribution of the data they are trained on. With real-world limitations, such as finite network capacity and training set size, they inevitably suffer a yet unavoidable technical failure: mode collapse. GAN-generat...

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Bibliographic Details
Other Authors:	Jain, Niharika (Author)
Format:	Dissertation
Language:	English
Published:	2020
Subjects:	Artificial intelligence Computer science Ethics bias data augmentation generative adversarial network machine learning society
Online Access:	http://hdl.handle.net/2286/R.I.57433

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