DeepZip: Lossless Data Compression using Recurrent Neural Networks
This addresses the need for efficient compression in data storage and transmission, though it is incremental as it builds on existing prediction-based methods with neural networks.
The authors tackled the problem of compressing sequential data like text and genomics by using recurrent neural network predictors combined with arithmetic coding, achieving better compression than Gzip on real datasets and near-optimal results on synthetic ones.
Sequential data is being generated at an unprecedented pace in various forms, including text and genomic data. This creates the need for efficient compression mechanisms to enable better storage, transmission and processing of such data. To solve this problem, many of the existing compressors attempt to learn models for the data and perform prediction-based compression. Since neural networks are known as universal function approximators with the capability to learn arbitrarily complex mappings, and in practice show excellent performance in prediction tasks, we explore and devise methods to compress sequential data using neural network predictors. We combine recurrent neural network predictors with an arithmetic coder and losslessly compress a variety of synthetic, text and genomic datasets. The proposed compressor outperforms Gzip on the real datasets and achieves near-optimal compression for the synthetic datasets. The results also help understand why and where neural networks are good alternatives for traditional finite context models