  Despite their tremendous successes, convolutional neural networks (CNNs)
incur high computational/storage costs and are vulnerable to adversarial
perturbations. Recent works on robust model compression address these
challenges by combining model compression techniques with adversarial training.
But these methods are unable to improve throughput (frames-per-second) on
real-life hardware while simultaneously preserving robustness to adversarial
perturbations. To overcome this problem, we propose the method of Generalized
Depthwise-Separable (GDWS) convolution -- an efficient, universal,
post-training approximation of a standard 2D convolution. GDWS dramatically
improves the throughput of a standard pre-trained network on real-life hardware
while preserving its robustness. Lastly, GDWS is scalable to large problem
sizes since it operates on pre-trained models and doesn't require any
additional training. We establish the optimality of GDWS as a 2D convolution
approximator and present exact algorithms for constructing optimal GDWS
convolutions under complexity and error constraints. We demonstrate the
effectiveness of GDWS via extensive experiments on CIFAR-10, SVHN, and ImageNet
datasets. Our code can be found at https://github.com/hsndbk4/GDWS.
