A Deep Learning Approach to Data Compression
We introduce Bit-Swap, a scalable and effective lossless data compression technique based on deep learning. It extends previous work on practical compression with latent variable models, based on bits-back coding and asymmetric numeral systems. In our experiments Bit-Swap is able to beat benchmark compressors on a highly diverse collection of images. We’re releasing code for the method and optimized models such that people can explore and advance this line of modern compression ideas. We also release a demo and a pre-trained model for Bit-Swap image compression and decompression on your own image. See the end of the post for a talk that covers how bits-back coding and Bit-Swap works.
Lossless compression for high-dimensional data
The goal is to design an effective lossless compression scheme that is scalable to high-dimensional data, like images. This is a matter of concurrently solving two problems:
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