# DARC: Differentiable ARchitecture Compression Source: [DARC: Differentiable ARchitecture Compression]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:39.127Z Authors: Shashank Singh, Ashish Khetan, Zohar Karnin Published: 2019-05-20T15:30:06.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()