# Understanding The Effectiveness of Lossy Compression in Machine Learning Training Sets

arXiv: https://arxiv.org/abs/2403.15953 · HTML: https://mlsanity.com/paper/2403.15953/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression](https://mlsanity.com/paper/2011.09017/) (2020)
- [FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data](https://mlsanity.com/paper/2001.06139/) (2020)
- [Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications](https://mlsanity.com/paper/2002.03742/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records](https://mlsanity.com/paper/2503.06096/) (2025)
- [Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling](https://mlsanity.com/paper/2606.15959/) (2026)
- [dreaMLearning: Data Compression Assisted Machine Learning](https://mlsanity.com/paper/2506.22190/) (2025)
- [Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance](https://mlsanity.com/paper/2511.08439/) (2025)
- [Transmission Factors for Lossy Compression of PDE Training Data: Measuring What Reaches a Trained Operator](https://mlsanity.com/paper/2610.06095/) (2026)

## Abstract (arXiv)

Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent in high performance computing (HPC). However, these methods depend on vast volumes of floating point data for training and validation which need methods to share the data on a wide area network (WAN) or to transfer it from edge devices to data centers. Data compression can be a solution to these problems, but an in-depth understanding of how lossy compression affects model quality is needed. Prior work largely considers a single application or compression method. We designed a systematic methodology for evaluating data reduction techniques for ML/AI, and we use it to perform a very comprehensive evaluation with 17 data reduction methods on 7 ML/AI applications to show modern lossy compression methods can achieve a 50-100x compression ratio improvement for a 1% or less loss in quality. We identify critical insights that guide the future use and design of lossy compressors for ML/AI.