Slim-CNN: A Light-Weight CNN for Face Attribute Prediction
We introduce a computationally-efficient CNN micro-architecture Slim Module to design a lightweight deep neural network, Slim-CNN, 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 multiple Slim Modules to devise a compact CNN, which still maintains very high accuracy. Additionally, Slim-CNN has a very low memory footprint, which makes it suitable for mobile and embedded applications. Experiments on the CelebA dataset show that Slim-CNN achieve an accuracy of 91.24% with 25x fewer parameters compared to MCNN-AUX and 100x fewer parameters when compared to DTML. This reduces the memory storage requirement of Slim-CNN by at least 87%. Furthermore, we compare Slim Modules with other well-known micro-architectures, such as Inception modules, residual blocks, Shuffle-Unit, and Inverted Residual units, and show it outperforms them in performance and in memory size, making it suitable for face-related tasks in embedded applications.