Abstract
This paper proposes a neural architecture search space using ResNet as a framework, with search objectives including parameters for convolution, pooling, fully connected layers, and connectivity of the residual network. In addition to recognition accuracy, this paper uses the loss value on the validation set as a secondary objective for optimization. The experimental results demonstrate that the search space of this paper together with the optimisation approach can find competitive network architectures on the MNIST, Fashion-MNIST and CIFAR100 datasets.
Abstract (translated)
本文提出了一种基于ResNet框架的神经架构搜索空间，其中搜索目标包括卷积层、池化层和全连接层的参数以及残差网络的连通性。除了识别准确率之外，该论文还使用验证集上的损失值作为优化的次要目标。实验结果表明，本文提出的搜索空间结合优化方法能够在MNIST、Fashion-MNIST和CIFAR100数据集上找到具有竞争力的网络架构。
URL
https://arxiv.org/abs/2505.01313
