Information Compression in the AI Era: Recent Advances and Future Challenges
arxiv(2024)
Abstract
This survey articles focuses on emerging connections between the fields of
machine learning and data compression. While fundamental limits of classical
(lossy) data compression are established using rate-distortion theory, the
connections to machine learning have resulted in new theoretical analysis and
application areas. We survey recent works on task-based and goal-oriented
compression, the rate-distortion-perception theory and compression for
estimation and inference. Deep learning based approaches also provide natural
data-driven algorithmic approaches to compression. We survey recent works on
applying deep learning techniques to task-based or goal-oriented compression,
as well as image and video compression. We also discuss the potential use of
large language models for text compression. We finally provide some directions
for future research in this promising field.
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