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In this work, we develop a neural architecture search algorithm, termedResbuilder, that develops ResNet architectures from scratch that achieve highaccuracy at moderate computational cost. It can also be used to modify existingarchitectures and has the capability to remove and insert ResNet blocks, inthis way searching for suitable architectures in the space of ResNetarchitectures. In our experiments on different image classification datasets,Resbuilder achieves close to state-of-the-art performance while savingcomputational cost compared to off-the-shelf ResNets. Noteworthy, we once tunethe parameters on CIFAR10 which yields a suitable default choice for all otherdatasets. We demonstrate that this property generalizes even to industrialapplications by applying our method with default parameters on a proprietaryfraud detection dataset.

ResBuilder: Automated Learning of Depth with Residual Structures