Abstract
We propose local binary convolution (LBC), an efficient
alternative to convolutional layers in standard convolutional
neural networks (CNN). The design principles of LBC are
motivated by local binary patterns (LBP). The LBC layer
comprises of a set of fixed sparse pre-defined binary convolutional filters that are not updated during the training process,
a non-linear activation function and a set of learnable linear weights. The linear weights combine the activated filter
responses to approximate the corresponding activated filter responses of a standard convolutional layer. The LBC
layer affords significant parameter savings, 9x to 169x in
the number of learnable parameters compared to a standard
convolutional layer. Furthermore, the sparse and binary nature of the weights also results in up to 9x to 169x savings in
model size compared to a standard convolutional layer. We
demonstrate both theoretically and experimentally that our
local binary convolution layer is a good approximation of a
standard convolutional layer. Empirically, CNNs with LBC
layers, called local binary convolutional neural networks
(LBCNN), achieves performance parity with regular CNNs
on a range of visual datasets (MNIST, SVHN, CIFAR-10, and
ImageNet) while enjoying significant computational savings