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 [31] and ResNet [8]. 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.