Making Sense of Big Data

A deep dive into the compression settings of deep learning’s most popular benchmark

Frankenstein’s Monster of datasets? Read on to find out. Image by author.

I’ve been on a mission lately to get more people thinking about how lossy compression affects their deep learning models [1]. In the process I spent a lot of time with ImageNet [2], which consists entirely of JPEG files, and I started noticing some peculiar compression settings. To see how systemic these odd settings are, I decided to survey the compression settings over the entire dataset. In this post, I report what I saw, including why I think some of these settings are weird, and show the statistics I computed for each of the relevant compression settings. …


Deep learning on JPEG and DCT domain data represents a promising new direction for research.

While working on my dissertation proposal, I had the opportunity to revisit my ICCV 2019 paper “Deep Residual Learning in the JPEG Transform Domain”. It was an interesting experience to look back on it after about a year and see how the field has evolved since then. In this article, I will give some details about the method we presented in the paper, then talk a little about the latest advances in DCT domain deep learning.

JPEG vs spatial throughput, more on this later. Image by author.


I expect many readers will be familiar with deep learning, the ubiquitous technique for modern machine learning. Fewer people, however, will be familiar with…

Max Ehrlich

Ph.D. Student at the University of Maryland

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