# EffNet: An Efficient Structure for Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1801.06434 · HTML: https://mlsanity.com/paper/1801.06434/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 9 of 9)

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- [Semi-Supervised End-To-End Contrastive Learning For Time Series Classification](https://mlsanity.com/paper/2310.08848/) (2023)
- [NL-CNN: A Resources-Constrained Deep Learning Model based on Nonlinear Convolution](https://mlsanity.com/paper/2102.00227/) (2021)
- [LB-CNN: An Open Source Framework for Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy](https://mlsanity.com/paper/2106.15350/) (2021)
- [Lightweight Fish Classification Model for Sustainable Marine Management: Indonesian Case](https://mlsanity.com/paper/2401.02278/) (2024)
- [Enhancing Context Through Contrast](https://mlsanity.com/paper/2401.03314/) (2024)

## Abstract (arXiv)

With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware. Slimmer models have therefore become a hot research topic with various approaches which vary from binary networks to revised convolution layers. We offer our contribution to the latter and propose a novel convolution block which significantly reduces the computational burden while surpassing the current state-of-the-art. Our model, dubbed EffNet, is optimised for models which are slim to begin with and is created to tackle issues in existing models such as MobileNet and ShuffleNet.