合成Petri Dish：一种新的体系结构快速搜索的代理模型
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
摘要：
Neural Architecture Search（NAS）探索了大量的建筑主题-这是一个计算密集型过程，通常涉及通过在大型网络中实例化每个主题来对每个主题进行地面实况评估，并使用数千个特定领域的数据样本来训练和评估网络。生物图案，如细胞，有时是从他们的自然环境中提取，并在人工培养皿设置研究的启发，本文提出了评估建筑图案的合成培养皿模型。在合成Petri Dish中，架构基元在非常小的网络中实例化，并使用非常少的学习合成数据样本进行评估（以有效地近似整个问题的性能）。合成Petri Dish中基序的相对性能可以替代它们的地面真实性能，从而加速NAS最昂贵的步骤。与其他基于神经网络的预测模型不同，合成Petri Dish通过在人工环境中训练实际的基序来预测基序性能，从而从其真实的内在属性中得出预测。本文中的实验表明，合成Petri Dish因此可以预测新图案的性能，具有显着更高的准确性，特别是当地面真实数据不足时。我们的希望是，这项工作可以激发一个新的研究方向，在另一种控制设置中研究模型的提取组件的性能。
英文摘要：
Neural Architecture Search (NAS) explores a large space of architectural motifs -- a compute-intensive process that often involves ground-truth evaluation of each motif by instantiating it within a large network, and training and evaluating the network with thousands of domain-specific data samples. Inspired by how biological motifs such as cells are sometimes extracted from their natural environment and studied in an artificial Petri dish setting, this paper proposes the Synthetic Petri Dish model for evaluating architectural motifs. In the Synthetic Petri Dish, architectural motifs are instantiated in very small networks and evaluated using very few learned synthetic data samples (to effectively approximate performance in the full problem). The relative performance of motifs in the Synthetic Petri Dish can substitute for their ground-truth performance, thus accelerating the most expensive step of NAS. Unlike other neural network-based prediction models that parse the structure of the motif to estimate its performance, the Synthetic Petri Dish predicts motif performance by training the actual motif in an artificial setting, thus deriving predictions from its true intrinsic properties. Experiments in this paper demonstrate that the Synthetic Petri Dish can therefore predict the performance of new motifs with significantly higher accuracy, especially when insufficient ground truth data is available. Our hope is that this work can inspire a new research direction in studying the performance of extracted components of models in an alternative controlled setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
