# Kymatio: Scattering Transforms in Python

arXiv: https://arxiv.org/abs/1812.11214 · HTML: https://mlsanity.com/paper/1812.11214/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2018.

## Built on by (most cited first, 30 of 58)

- [Learning curves of generic features maps for realistic datasets with a teacher-student model](https://mlsanity.com/paper/2102.08127/) (2021)
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- [NOMAD: Nonlinear Manifold Decoders for Operator Learning](https://mlsanity.com/paper/2206.03551/) (2022)
- [Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series](https://mlsanity.com/paper/2105.00899/) (2021)
- [Gaussian Universality of Perceptrons with Random Labels](https://mlsanity.com/paper/2205.13303/) (2022)
- [Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation](https://mlsanity.com/paper/2302.08923/) (2023)
- [Parametric Scattering Networks](https://mlsanity.com/paper/2107.09539/) (2021)
- [A Bayesian neural network predicts the dissolution of compact planetary systems](https://mlsanity.com/paper/2101.04117/) (2021)
- [ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets](https://mlsanity.com/paper/2108.13939/) (2021)
- [InQSS: a speech intelligibility and quality assessment model using a multi-task learning network](https://mlsanity.com/paper/2111.02585/) (2021)
- [Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transforms](https://mlsanity.com/paper/2104.11244/) (2021)
- [Phase Collapse in Neural Networks](https://mlsanity.com/paper/2110.05283/) (2021)
- [Riesz feature representation: scale equivariant scattering network for classification tasks](https://mlsanity.com/paper/2307.08467/) (2023)
- [Divergences in Color Perception between Deep Neural Networks and Humans](https://mlsanity.com/paper/2309.05809/) (2023)
- [Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems](https://mlsanity.com/paper/2408.07828/) (2024)
- [Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves](https://mlsanity.com/paper/2410.00984/) (2024)
- [Monogenic Wavelet Scattering Network for Texture Image Classification](https://mlsanity.com/paper/2202.12491/) (2022)
- [Physics-assisted Generative Adversarial Network for X-Ray Tomography](https://mlsanity.com/paper/2204.03703/) (2022)
- [TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring](https://mlsanity.com/paper/2207.11290/) (2022)
- [Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN](https://mlsanity.com/paper/2303.11423/) (2023)
- [WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database](https://mlsanity.com/paper/2402.17775/) (2024)
- [WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection](https://mlsanity.com/paper/2602.02980/) (2026)
- [Mesostructures: Beyond Spectrogram Loss in Differentiable Time-Frequency Analysis](https://mlsanity.com/paper/2301.10183/) (2023)
- [Wavelets Beat Monkeys at Adversarial Robustness](https://mlsanity.com/paper/2304.09403/) (2023)
- [Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning](https://mlsanity.com/paper/2506.09674/) (2025)
- [Efficient Hybrid Network: Inducting Scattering Features](https://mlsanity.com/paper/2203.15392/) (2022)
- [Differentiable Time-Frequency Scattering on GPU](https://mlsanity.com/paper/2204.08269/) (2022)
- [Multi-Resolution Online Deterministic Annealing: A Hierarchical and Progressive Learning Architecture](https://mlsanity.com/paper/2212.08189/) (2022)
- [Perceptual-Neural-Physical Sound Matching](https://mlsanity.com/paper/2301.02886/) (2023)
- [MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation](https://mlsanity.com/paper/2303.12130/) (2023)

## Abstract (arXiv)

The wavelet scattering transform is an invariant 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. All transforms may be executed on a GPU (in addition to CPU), offering a considerable speed up over CPU implementations. The package also has a small memory footprint, resulting inefficient memory usage. The source code, documentation, and examples are available undera BSD license at https://www.kymat.io/