  Extracting information from stochastic fields or textures is a ubiquitous
task in science, from exploratory data analysis to classification and parameter
estimation. From physics to biology, it tends to be done either through a power
spectrum analysis, which is often too limited, or the use of convolutional
neural networks (CNNs), which require large training sets and lack
interpretability. In this paper, we advocate for the use of the scattering
transform (Mallat 2012), a powerful statistic which borrows mathematical ideas
from CNNs but does not require any training, and is interpretable. We show that
it provides a relatively compact set of summary statistics with visual
interpretation and which carries most of the relevant information in a wide
range of scientific applications. We present a non-technical introduction to
this estimator and we argue that it can benefit data analysis, comparison to
models and parameter inference in many fields of science. Interestingly,
understanding the core operations of the scattering transform allows one to
decipher many key aspects of the inner workings of CNNs.
