2020
JMLR
JMLR 2020
Kymatio: Scattering Transforms in Python
Abstract
The wavelet scattering transform is an invariant and stable signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks, including PyTorch and TensorFlow/Keras. The transforms are implemented on both CPUs and GPUs, the latter offering a significant speedup over the former. The package also has a small memory footprint. Source code, documentation, and examples are available under a BSD license at https://www.kymat.io. [abs] [ pdf ][ bib ] [ code ] © JMLR 2020. (edit, beta)
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning
🧭
Keyword Pioneer
— wavelet scattering transform
🐣
Hot Topic Early Bird
— signal processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Mathieu Andreux
,
Tomás Angles
,
Georgios Exarchakis
,
Roberto Leonarduzzi
,
Gaspar Rochette
,
Louis Thiry
,
John Zarka
,
Stephane Mallat
,
Joakim Andén
,
Eugene Belilovsky
,
Joan Bruna
,
Vincent Lostanlen
,
Muawiz Chaudhary
,
Matthew J. Hirn
,
Edouard Oyallon
,
Sixin Zhang
,
Carmine Cella
,
Michael Eickenberg