  We introduce a computationally-efficient CNN micro-architecture Slim Module
to design a lightweight deep neural network Slim-Net for face attribute
prediction. Slim Modules are constructed by assembling depthwise separable
convolutions with pointwise convolution to produce a computationally efficient
module. The problem of facial attribute prediction is challenging because of
the large variations in pose, background, illumination, and dataset imbalance.
We stack these Slim Modules to devise a compact CNN which still maintains very
high accuracy. Additionally, the neural network has a very low memory footprint
which makes it suitable for mobile and embedded applications. Experiments on
the CelebA dataset show that Slim-Net achieves an accuracy of 91.24% with at
least 25 times fewer parameters than comparably performing methods, which
reduces the memory storage requirement of Slim-net by at least 87%.
