Deep convolutional neural networks have demonstrated promising performance on image classification tasks, but the manual design process becomes more and more complex due to the fast depth growth and the increasingly complex topologies of convolutional neural networks. As a result, neural architecture search has emerged to automatically design convolutional neural networks that outperform handcrafted counterparts. However, the computational cost is immense, e.g. 22,400 GPU-days and 2,000 GPU-days for two outstanding neural architecture search works named NAS and NASNet, respectively, which motivates this work. A new effective and efficient surrogate-assisted particle swarm optimisation algorithm is proposed to automatically evolve convolutional neural networks. This is achieved by proposing a novel surrogate model, a new method of creating a surrogate dataset and a new encoding strategy to encode variable-length blocks of convolutional neural networks, all of which are integrated into a particle swarm optimisation algorithm to form the proposed method. The proposed method shows its effectiveness by achieving competitive error rates of 3.49% on the CIFAR-10 dataset, 18.49% on the CIFAR-100 dataset, and 1.82% on the SVHN dataset. The convolutional neural network blocks are efficiently learned by the proposed method from CIFAR-10 within 3 GPU-days due to the acceleration achieved by the surrogate model and the surrogate dataset to avoid the training of 80.1% of convolutional neural network blocks represented by the particles. Without any further search, the evolved blocks from CIFAR-10 can be successfully transferred to CIFAR-100 and SVHN, which exhibits the transferability of the block learned by the proposed method.

中文翻译：

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用于图像分类的进化可变长度可转移块的代理辅助粒子群优化

深度卷积神经网络在图像分类任务上表现出良好的性能，但由于卷积神经网络的快速深度增长和日益复杂的拓扑结构，手动设计过程变得越来越复杂。因此，出现了神经架构搜索来自动设计优于手工制作的卷积神经网络。然而，计算成本是巨大的，例如 22,400 GPU 天和 2,000 GPU 天分别用于两个名为 NAS 和 NASNet 的优秀神经架构搜索工作，这激发了这项工作。提出了一种新的有效且高效的代理辅助粒子群优化算法来自动进化卷积神经网络。这是通过提出一个新的代理模型来实现的，一种创建代理数据集的新方法和一种新的编码策略来编码卷积神经网络的可变长度块，所有这些都被集成到粒子群优化算法中以形成所提出的方法。所提出的方法通过在 CIFAR-10 数据集上实现 3.49% 的竞争错误率、在 CIFAR-100 数据集上实现 18.49% 和在 SVHN 数据集上实现 1.82% 来显示其有效性。由于代理模型和代理数据集实现的加速避免了由粒子表示的 80.1% 的卷积神经网络块的训练，因此卷积神经网络块在 3 个 GPU 天内通过 CIFAR-10 提出的方法有效学习. 无需任何进一步搜索，CIFAR-10 的进化块可以成功转移到 CIFAR-100 和 SVHN，