Convolutional neural networks (CNNs) are the backbones of deep learning paradigms for numerous vision tasks. Early advancements in CNN architectures are primarily driven by human expertise and by elaborate design processes. Recently, neural architecture search was proposed with the aim of automating the network design process and generating task-dependent architectures. While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches. To overcome these limitations, we propose an evolutionary algorithm for searching neural architectures under multiple objectives, such as classification performance and floating point operations (FLOPs). The proposed method addresses the first shortcoming by populating a set of architectures to approximate the entire Pareto frontier through genetic operations that recombine and modify architectural components progressively. Our approach improves computational efficiency by carefully down-scaling the architectures during the search as well as reinforcing the patterns commonly shared among past successful architectures through Bayesian model learning. The integration of these two main contributions allows an efficient design of architectures that are competitive and in most cases outperform both manually and automatically designed architectures on benchmark image classification datasets: CIFAR, ImageNet and human chest X-ray. The flexibility provided from simultaneously obtaining multiple architecture choices for different compute requirements further differentiates our approach from other methods in the literature.

中文翻译：

---

用于图像分类的深度卷积神经网络的多目标进化设计

卷积神经网络 (CNN) 是许多视觉任务的深度学习范式的支柱。CNN 架构的早期进步主要是由人类专业知识和精心设计的过程驱动的。最近，提出了神经架构搜索，目的是使网络设计过程自动化并生成依赖于任务的架构。虽然现有方法在图像分类方面取得了有竞争力的性能，但它们不太适合计算预算有限的问题，原因有两个：（1）获得的架构要么仅针对分类性能进行了优化，要么仅针对一种部署场景进行了优化；(2) 在大多数方法中，搜索过程需要大量的计算资源。为了克服这些限制，我们提出了一种进化算法，用于在多个目标下搜索神经架构，例如分类性能和浮点运算 (FLOP)。所提出的方法通过逐步重组和修改架构组件的遗传操作填充一组架构来近似整个帕累托边界，从而解决了第一个缺点。我们的方法通过在搜索过程中仔细缩小架构以及通过贝叶斯模型学习加强过去成功架构之间普遍共享的模式来提高计算效率。这两个主要贡献的集成允许有效设计具有竞争力的架构，并且在大多数情况下在基准图像分类数据集上优于手动和自动设计的架构：CIFAR、ImageNet 和人体胸部 X 射线。针对不同的计算要求同时获得多个架构选择所提供的灵活性进一步将我们的方法与文献中的其他方法区分开来。