论文标题
NAAP-440数据集和神经体系结构准确性预测的基线
NAAP-440 Dataset and Baseline for Neural Architecture Accuracy Prediction
论文作者
论文摘要
神经体系结构搜索（NAS）已成为开发和发现针对不同目标平台和目的的新神经体系结构的常见方法。但是，扫描搜索空间由许多候选体系结构的长期培训过程组成，这在计算资源和时间方面是昂贵的。回归算法是预测候选体系结构准确性的常见工具，可以极大地加速搜索过程。我们旨在提出一个新的基准，该基线将支持回归算法的开发，该算法可以通过其计划来预测架构的准确性，或者仅通过对其进行最少数量的时代培训。因此，我们介绍了440个神经体系结构的NAAP-440数据集，这些数据集使用固定配方在CIFAR10上进行了训练。我们的实验表明，通过使用现成的回归算法并运行多达10％的训练过程，不仅可以确切地预测体系结构的准确性，而且还可以通过最少的单程违规行为来维持其准确性顺序。这种方法可以作为加速基于NAS的研究的强大工具，从而大大提高其效率。研究中使用的数据集和代码已公开。
Neural architecture search (NAS) has become a common approach to developing and discovering new neural architectures for different target platforms and purposes. However, scanning the search space is comprised of long training processes of many candidate architectures, which is costly in terms of computational resources and time. Regression algorithms are a common tool to predicting a candidate architecture's accuracy, which can dramatically accelerate the search procedure. We aim at proposing a new baseline that will support the development of regression algorithms that can predict an architecture's accuracy just from its scheme, or by only training it for a minimal number of epochs. Therefore, we introduce the NAAP-440 dataset of 440 neural architectures, which were trained on CIFAR10 using a fixed recipe. Our experiments indicate that by using off-the-shelf regression algorithms and running up to 10% of the training process, not only is it possible to predict an architecture's accuracy rather precisely, but that the values predicted for the architectures also maintain their accuracy order with a minimal number of monotonicity violations. This approach may serve as a powerful tool for accelerating NAS-based studies and thus dramatically increase their efficiency. The dataset and code used in the study have been made public.
