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Predictive Coding For Lossless Dataset Compression

Citation Author(s):
Madeleine Barowsky, Alexander Mariona, Flavio P. Calmon
Submitted by:
Madeleine Barowsky
Last updated:
23 June 2021 - 11:40am
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Madeleine Barowsky
Paper Code:
IVMSP-3.1
Categories:

Abstract 

Abstract: 

Lossless compression of datasets is a problem of significant theoretical and practical interest. It appears naturally in the task of storing, sending, or archiving large collections of information for scientific research. We can greatly improve encoding bitrate if we allow the compression of the original dataset to decompress to a permutation of the data. We prove the equivalence of dataset compression to compressing a permutation-invariant structure of the data and implement such a scheme via predictive coding. We benchmark our compression procedure against state-of-the-art compression utilities on the popular machine-learning datasets MNIST and CIFAR-10 and outperform for multiple parameter sets.

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Dataset Files

Dataset Compression ICASSP poster.pdf

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