Dropbox, Inc.

Anatoly Yakovenko

Jongmin Baek

Daniel R Horn

Lossless compression of a content item using a neural network trained on content item cohorts. A computing system includes a neural network that is used to train a plurality of symbol prediction models. Each symbol prediction model is trained based on a corresponding cohort of content items. A particular symbol prediction model of the models trained is selected based on an intrinsic characteristic of a particular content item to be losslessly compressed such as, for example, the type or file extension of the content item. The content item is then losslessly compressed based on a set of symbol predictions fed to an arithmetic coder that are generated using the particular symbol prediction model selected.

Lossless compression of a content item using a neural network trained on content item cohorts

Techniques for general-purpose lossless data compression using a neural network including compressing an original content item to a baseline lossless compressed data format. The baseline lossless compressed data format is binarized to a binarized format. The binarized format is arithmetically coded based on probability estimates from a neural network probability estimator. The neural network probability estimator generates the probability estimates for current symbols of the binarized format to be arithmetically coded based on symbols of the binarized format that have already been arithmetically coded.

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San Francisco, CA, United States of America

Anatoly Yakovenko