Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including computer vision, system configuration, and question-answering. However, DNNs are expensive to develop, both in intellectual effort (e.g., devising new architectures) and computational costs (e.g., training). Reusing DNNs is a promising direction to amortize costs within a company and across the computing industry. As with any new technology, however, there are many challenges in reusing DNNs. These challenges include both missing technical capabilities and missing engineering practices. This vision paper describes challenges in current approaches to DNN re-use. We summarize studies of re-use failures across the spectrum of re-use techniques, including conceptual (e.g., reusing based on a research paper), adaptation (e.g., re-using by building on an existing implementation), and deployment (e.g., direct re-use on a new device). We outline possible advances that would improve each kind of re-use.

翻译：深度神经网络在计算机视觉、系统配置和问答等多个领域实现了最先进的性能。然而，深度神经网络的开发成本高昂，既体现在智力投入（如设计新架构）方面，也体现在计算成本（如训练）方面。复用深度神经网络是分摊企业内部及整个计算行业成本的一个有前景的方向。然而，与任何新技术一样，复用深度神经网络存在诸多挑战，既包括缺失的技术能力，也包括缺失的工程实践。本愿景论文描述了当前深度神经网络复用方法中的挑战。我们总结了复用技术全程中复用失败的研究，涵盖概念性复用（例如基于研究论文的复用）、适应性复用（例如基于现有实现进行构建）以及部署性复用（例如在新设备上直接复用）。我们概述了可能改进各类复用方式的潜在进展。