  Convolutional Neural Networks (CNNs) filter the input data using spatial
convolution operators with compact stencils. Commonly, the convolution
operators couple features from all channels, which leads to immense
computational cost in the training of and prediction with CNNs. To improve the
efficiency of CNNs, we introduce lean convolution operators that reduce the
number of parameters and computational complexity, and can be used in a wide
range of existing CNNs. Here, we exemplify their use in residual networks
(ResNets), which have been very reliable for a few years now and analyzed
intensively. In our experiments on three image classification problems, the
proposed LeanResNet yields results that are comparable to other recently
proposed reduced architectures using similar number of parameters.
