EffNet: An Efficient Structure for Convolutional Neural Networks
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.
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Tasks
CIFAR-10
SVHN
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Channel Shuffle •
Convolution •
Dense Connections •
Depthwise Convolution •
Depthwise Separable Convolution •
Global Average Pooling •
Max Pooling •
MobileNetV1 •
Pointwise Convolution •
ReLU •
Residual Connection •
ShuffleNet •
ShuffleNet Block •
Softmax
