QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures
This work addresses the need for efficient deep learning models for memory- and computationally constrained systems, though it is incremental as it builds on existing methods like depthwise separable convolutions and parametric ReLUs.
The authors tackled the problem of creating a fast and accurate deep network architecture, resulting in QuickNet, which achieves 95.7% accuracy on CIFAR-10 while being faster and using fewer parameters than prior models like SqueezeNet.
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.