QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures

TL;DR

QuickNet is a novel deep neural network architecture that achieves higher accuracy and faster inference with fewer parameters by utilizing depthwise separable convolutions and parametric ReLUs, suitable for memory and computationally constrained systems.

Contribution

The paper introduces QuickNet, a new architecture that combines depthwise separable convolutions and parametric ReLUs to improve speed and accuracy over existing models.

Findings

Achieves 95.7% accuracy on CIFAR-10

Faster inference than comparable architectures

Uses fewer parameters for memory efficiency

Abstract

We present QuickNet, a fast and accurate network architecture that is both faster and significantly more accurate than other fast deep architectures like SqueezeNet. Furthermore, it uses less parameters than previous networks, making it more memory efficient. We do this by making two major modifications to the reference Darknet model (Redmon et al, 2015): 1) The use of depthwise separable convolutions and 2) The use of parametric rectified linear units. We make the observation that parametric rectified linear units are computationally equivalent to leaky rectified linear units at test time and the observation that separable convolutions can be interpreted as a compressed Inception network (Chollet, 2016). Using these observations, we derive a network architecture, which we call QuickNet, that is both faster and more accurate than previous models. Our architecture provides at least four major advantages: (1) A smaller model size, which is more tenable on memory constrained systems; (2) A significantly faster network which is more tenable on computationally constrained systems; (3) A high accuracy of 95.7 percent on the CIFAR-10 Dataset which outperforms all but one result published so far, although we note that our works are orthogonal approaches and can be combined (4) Orthogonality to previous model compression approaches allowing for further speed gains to be realized.