  We present a comprehensive, stacking-based framework for combining deep
learning with good old-fashioned machine learning, called Deep GOld. Our
framework involves ensemble selection from 51 retrained pretrained deep
networks as first-level models, and 10 machine-learning algorithms as
second-level models. Enabled by today's state-of-the-art software tools and
hardware platforms, Deep GOld delivers consistent improvement when tested on
four image-classification datasets: Fashion MNIST, CIFAR10, CIFAR100, and Tiny
ImageNet. Of 120 experiments, in all but 10 Deep GOld improved the original
networks' performance.
