# Compressing the Input for CNNs with the First-Order Scattering Transform

arXiv: https://arxiv.org/abs/1809.10200 · HTML: https://mlsanity.com/paper/1809.10200/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods](https://mlsanity.com/paper/2101.07528/) (2021)
- [Compressed Vision for Efficient Video Understanding](https://mlsanity.com/paper/2210.02995/) (2022)
- [Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning](https://mlsanity.com/paper/2106.06401/) (2021)
- [DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer](https://mlsanity.com/paper/2211.08577/) (2022)
- [ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges](https://mlsanity.com/paper/2304.14919/) (2023)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks](https://mlsanity.com/paper/2209.11740/) (2022)
- [Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets](https://mlsanity.com/paper/2303.06797/) (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)
- [Interferometric Graph Transform for Community Labeling](https://mlsanity.com/paper/2106.05875/) (2021)

## Abstract (arXiv)

We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence that in the case of natural images and sufficiently small translation invariance, this transform preserves most of the signal information needed for classification while substantially reducing the spatial resolution and total signal size. We demonstrate that cascading a CNN with this representation performs on par with ImageNet classification models, commonly used in downstream tasks, such as the ResNet-50. We subsequently apply our trained hybrid ImageNet model as a base model on a detection system, which has typically larger image inputs. On Pascal VOC and COCO detection tasks we demonstrate improvements in the inference speed and training memory consumption compared to models trained directly on the input image.