Training deep neural network (DNN) models, which has become an important task
in today's software development, is often costly in terms of computational
resources and time. With the inspiration of software reuse, building DNN models
through reusing existing ones has gained increasing attention recently. Prior
approaches to DNN model reuse have two main limitations: 1) reusing the entire
model, while only a small part of the model's functionalities (labels) are
required, would cause much overhead (e.g., computational and time costs for
inference), and 2) model reuse would inherit the defects and weaknesses of the
reused model, and hence put the new system under threats of security attack. To
solve the above problem, we propose SeaM, a tool that re-engineers a trained
DNN model to improve its reusability. Specifically, given a target problem and
a trained model, SeaM utilizes a gradient-based search method to search for the
model's weights that are relevant to the target problem. The re-engineered
model that only retains the relevant weights is then reused to solve the target
problem. Evaluation results on widely-used models show that the re-engineered
models produced by SeaM only contain 10.11% weights of the original models,
resulting 42.41% reduction in terms of inference time. For the target problem,
the re-engineered models even outperform the original models in classification
accuracy by 5.85%. Moreover, reusing the re-engineered models inherits an
average of 57% fewer defects than reusing the entire model. We believe our
approach to reducing reuse overhead and defect inheritance is one important
step forward for practical model reuse.

SeaM 是一种改进现有 DNN 模型可重用性的工具，它使用基于梯度的搜索方法搜索模型的相关权重，从而重建模型以仅保留与目标问题相关的权重，并且相较于原始模型，在分类准确性上提高了 5.85％，并且重复使用重建模型时缺陷继承平均减少了 57％。