Monumental advances in deep learning have led to unprecedented achievements across various domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multi-objective distributed NAS framework that optimizes for both task performance and "introspectability," a surrogate metric for aspects of interpretability. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by domain experts. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for task error and introspectability leads to more disentangled and debuggable architectures that perform within tolerable error.

翻译：深度学习领域的重大进展已在多个领域取得了前所未有的成就。尽管深度神经网络的性能毋庸置疑，但此类模型的架构设计与可解释性仍是重要课题。已有研究通过神经架构搜索（NAS）方法实现神经网络架构的自动化设计。近期进展利用分布式计算与新型优化算法使这些方法更具实用性，然而针对可解释性进行架构优化的研究仍十分有限。为此，我们提出一种多目标分布式NAS框架，该框架同时优化任务性能与“内省性”这一可解释性方面的替代指标。我们采用非支配排序遗传算法（NSGA-II）与可解释人工智能（XAI）技术，对领域专家更易理解的架构给予奖励。该框架在多个图像分类数据集上进行了评估。实验表明，联合优化任务错误率与内省性能够生成更解耦且更易调试的架构，且其性能保持在可接受误差范围内。