# WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices

Source: [WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices](<https://www.sota2.com/research/paper/68fa651e1e209131cea08812>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:19:52.933Z

Authors: Li Jing, Rumen Dangovski, Marin Soljacic

Published: 2018-11-28T16:04:30.000Z

## Abstract

We present a logarithmic-scale efficient convolutional neural network architecture for edge devices, named WaveletNet. Our model is based on the well-known depthwise convolution, and on two new layers, which we introduce in this work: a wavelet convolution and a depthwise fast wavelet transform. By breaking the symmetry in channel dimensions and applying a fast algorithm, WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D) factor, where D is the number of channels. Experiments on CIFAR-10 and ImageNet classification show superior and comparable performances of WaveletNet compared to state-of-the-art models such as MobileNetV2.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1811.11644>)

[PDF](<https://arxiv.org/pdf/1811.11644.pdf>)