Analysis of Lossy Generative Data Compression for Robust Remote Deep Inference-Reference-Cited by-同舟云学术

Analysis of Lossy Generative Data Compression for Robust Remote Deep Inference

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning
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Author:

Williams Mathew¹^ORCID,Kokalj-Filipovic Silvija¹^ORCID,Rodriguez Armani¹^ORCID

Affiliation:

1. Rowan University, Glassboro, NJ, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3586209.3591404

Reference24 articles.

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4. H. Edelsbrunner and D. Morozov . 2014. Persistent homology: theory and practice . In European Congress of Mathematics. H. Edelsbrunner and D. Morozov. 2014. Persistent homology: theory and practice. In European Congress of Mathematics.

5. M. Ehrlich , L. Davis , S.N. Lim , and A. Shrivastava . 2021 . Analyzing and Mitigating JPEG Compression Defects in Deep Learning. In IEEE/CVF Intern. Conf. on Computer Vision. M. Ehrlich, L. Davis, S.N. Lim, and A. Shrivastava. 2021. Analyzing and Mitigating JPEG Compression Defects in Deep Learning. In IEEE/CVF Intern. Conf. on Computer Vision.

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