Data compression with machine learning

  • Does machine learning work better with compressed images?

    ML algorithms work on raw images pixels without considering images are mostly stored in a compressed form.
    Thus, training complexity is handicapped by the image decompression process..

  • How can data be compressed?

    Compression is done by a program that uses functions or an algorithm to effectively discover how to reduce the size of the data.
    For example, an algorithm might represent a string of bits with a smaller string of bits by using a 'reference dictionary' for conversion between them..

  • Text compression techniques

    The gzip compression algorithm is popular as it has a great compression ratio while not requiring a long compression time and a lot of computing resources.
    These characteristics make gzip a good all-around compression method that serves as a great benchmark..

There is a close connection between machine learning and compression. A system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution).

Demo

Compress your own image using Bit-Swap.
Clone the GitHub repository onhttps://github.com/fhkingma/bitswap andrun the script demo_compress.py and demo_decompress.py.
The scriptdemo_compress.py will compress using Bit-Swap and compare it against GNUGzip, bzip2, LZMA, PNG and WebP compression.
The script demo_decompress.pywill decompress a Bit-Swap co.

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Our Contribution

While latent variable models can be designed to be complex density estimators,restricting the model to fully factorized distributions, however, cansignificantly limit model flexibility.
Therefore, we propose employinghierarchical latent variable models, which typically have greater modellingcapacity than models with a single latent layer.
We extend.

How do compression algorithms work?

Most existing compression algorithms utilize the overall characteristics of the entire time series to achieve high compression ratio, but ignore local contexts around individual points

In this way, they are effective for certain data patterns, and may suffer inherent pattern changes in real-world time series


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