# HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs

## TL;DR

HetConv introduces heterogeneous kernels into CNNs, significantly reducing FLOPs and parameters while maintaining or improving accuracy, demonstrated on VGG and ResNet architectures.

## Contribution

The paper proposes HetConv, a novel convolution method using heterogeneous kernels that enhances efficiency and accuracy over standard and group convolutions.

## Key findings

- Achieves 3X to 8X FLOPs reduction in CNNs
- Maintains or improves accuracy with HetConv
- Outperforms group/depthwise convolutions in efficiency

## Abstract

We present a novel deep learning architecture in which the convolution operation leverages heterogeneous kernels. The proposed HetConv (Heterogeneous Kernel-Based Convolution) reduces the computation (FLOPs) and the number of parameters as compared to standard convolution operation while still maintaining representational efficiency. To show the effectiveness of our proposed convolution, we present extensive experimental results on the standard convolutional neural network (CNN) architectures such as VGG \cite{vgg2014very} and ResNet \cite{resnet}. We find that after replacing the standard convolutional filters in these architectures with our proposed HetConv filters, we achieve 3X to 8X FLOPs based improvement in speed while still maintaining (and sometimes improving) the accuracy. We also compare our proposed convolutions with group/depth wise convolutions and show that it achieves more FLOPs reduction with significantly higher accuracy.