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An Introduction to Neural Data Compression
Loot Price: R1,790
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An Introduction to Neural Data Compression
Series: Foundations and Trends® in Computer Graphics and Vision
Expected to ship within 10 - 15 working days
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The goal of data compression is to reduce the number of bits needed
to represent useful information. Neural, or learned compression, is
the application of neural networks and related machine learning
techniques to this task. This monograph aims to serve as an entry
point for machine learning researchers interested in compression by
reviewing the prerequisite background and representative methods in
neural compression. Neural compression is the application of neural
networks and other machine learning methods to data compression.
Recent advances in statistical machine learning have opened up new
possibilities for data compression, allowing compression algorithms
to be learned end-to-end from data using powerful generative models
such as normalizing flows, variational autoencoders, diffusion
probabilistic models, and generative adversarial networks. This
monograph introduces this field of research to a broader machine
learning audience by reviewing the necessary background in
information theory (e.g., entropy coding, rate-distortion theory)
and computer vision (e.g., image quality assessment, perceptual
metrics), and providing a curated guide through the essential ideas
and methods in the literature thus far. Instead of surveying the
vast literature, essential concepts and methods in neural
compression are covered, with a reader in mind who is versed in
machine learning but not necessarily data compression.
General
Imprint: |
Now Publishers Inc
|
Country of origin: |
United States |
Series: |
Foundations and Trends® in Computer Graphics and Vision |
Release date: |
April 2023 |
First published: |
2023 |
Authors: |
Yibo Yang
• Stephan Mandt
• Lucas Theis
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Dimensions: |
234 x 156mm (L x W) |
Pages: |
100 |
ISBN-13: |
978-1-63828-174-0 |
Categories: |
Books
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LSN: |
1-63828-174-2 |
Barcode: |
9781638281740 |
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