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.

翻译：本文提出一种名为Resbuilder的神经架构搜索算法，该算法能从零开始构建以中等计算成本实现高精度的ResNet架构。该算法还可用于修改现有架构，具备移除和插入ResNet残差块的能力，从而在ResNet架构空间内搜索合适的网络结构。在多个图像分类数据集上的实验表明，相较于现成的ResNet模型，Resbuilder在降低计算成本的同时取得了接近最优的性能。值得注意的是，我们仅在CIFAR10数据集上进行一次参数调优，所得默认参数即可适用于所有其他数据集。通过将默认参数应用于专有欺诈检测数据集，我们证明该泛化特性甚至可推广至工业应用场景。