  In many learning situations, resources at inference time are significantly
more constrained than resources at training time. This paper studies a general
paradigm, called Differentiable ARchitecture Compression (DARC), that combines
model compression and architecture search to learn models that are
resource-efficient at inference time. Given a resource-intensive base
architecture, DARC utilizes the training data to learn which sub-components can
be replaced by cheaper alternatives. The high-level technique can be applied to
any neural architecture, and we report experiments on state-of-the-art
convolutional neural networks for image classification. For a WideResNet with
$97.2\%$ accuracy on CIFAR-10, we improve single-sample inference speed by
$2.28\times$ and memory footprint by $5.64\times$, with no accuracy loss. For a
ResNet with $79.15\%$ Top1 accuracy on ImageNet, we improve batch inference
speed by $1.29\times$ and memory footprint by $3.57\times$ with $1\%$ accuracy
loss. We also give theoretical Rademacher complexity bounds in simplified
cases, showing how DARC avoids overfitting despite over-parameterization.
