ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing
This is the artifact for the ICSE 2022 paper “ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing”.
Transfer learning is a popular software reuse technique in the deep learning community that enables developers to build custom models (students) based on sophisticated pretrained models (teachers). However, like vulnerability inheritance in traditional software reuse, some defects in the teacher model may also be inherited by students, such as well-known adversarial vulnerabilities and backdoors. Reducing such defects is challenging since the student is unaware of how the teacher is trained and/or attacked.
In this paper, we propose ReMoS, a relevant model slicing technique to reduce defect inheritance during transfer learning while retaining useful knowledge from the teacher model. Specifically, ReMoS computes a model