  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.
