  In this work, we develop a neural architecture search algorithm, termed
Resbuilder, that develops ResNet architectures from scratch that achieve high
accuracy at moderate computational cost. It can also be used to modify existing
architectures and has the capability to remove and insert ResNet blocks, in
this way searching for suitable architectures in the space of ResNet
architectures. In our experiments on different image classification datasets,
Resbuilder achieves close to state-of-the-art performance while saving
computational cost compared to off-the-shelf ResNets. Noteworthy, we once tune
the parameters on CIFAR10 which yields a suitable default choice for all other
datasets. We demonstrate that this property generalizes even to industrial
applications by applying our method with default parameters on a proprietary
fraud detection dataset.
