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Reusing Deep Learning Models: Challenges and Directions in Software Engineering

conference contribution
posted on 2023-07-06, 03:15 authored by James C. DavisJames C. Davis, Purvish JajalPurvish Jajal, Wenxin JiangWenxin Jiang, Taylor R SchorlemmerTaylor R Schorlemmer, Nicholas SynovicNicholas Synovic, George K. ThiruvathukalGeorge K. Thiruvathukal

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). Re-using 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 re-using 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., re-using 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.