Quantifying Manifolds: Do the Manifolds Learned by Generative Adversarial Networks Converge to the Real Data Manifold?

Chaudhuri, A; Simmons, A; Abdelrazek, Mohamed

Quantifying Manifolds: Do the Manifolds Learned by Generative Adversarial Networks Converge to the Real Data Manifold?

conference contribution

posted on 2025-01-16, 02:50 authored by A Chaudhuri, A Simmons, Mohamed AbdelrazekMohamed Abdelrazek

Quantifying Manifolds: Do the Manifolds Learned by Generative Adversarial Networks Converge to the Real Data Manifold?

History

Volume

15443 LNAI

Pagination

202-213

Location

Melbourne, Vic.

Publisher DOI

https://doi.org/10.1007/978-981-96-0351-0_15

Open access

No

Start date

2024-11-25

End date

2024-11-29

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9789819603503

Language

Eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

AI 2024 : Proceedings of the 37th Australasian Joint Conference on Artificial Intelligence 2024

Event

Australasian Joint Conference on Artificial Intelligence. (2024 : 37th : Melbourne, Vic.)

Publisher

Springer Nature Singapore

Place of publication

Singapore

Series

Lecture Notes in Computer Science

Publication URL

https://doi.org/10.1007/978-981-96-0351-0_15

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Keywords

Topological data analysis GAN convergence

Quantifying Manifolds: Do the Manifolds Learned by Generative Adversarial Networks Converge to the Real Data Manifold?

History

Volume

Pagination

Location

Publisher DOI

Open access

Start date

End date

ISSN

eISSN

ISBN-13

Language

Publication classification

Title of proceedings

Event

Publisher

Place of publication

Series

Publication URL

Usage metrics

Categories

Keywords

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