1. Introduction
The last decade has witnessed the great progress of deep learning in various fields, and a plethora of deep neural networks are developed and released publicly, with either their architectures and trained parameters (e.g., Tensorflow Hub , Pytorch Hub ) for research, or the prediction API (e.g., BigML, Amazon Machine Learning) as ML-as-a-Service (MLaaS) for commercial purposes. These off-the-shelf pre-trained models become extremely important resources for not only practitioners to solve their own problems, but also researchers to explore and exploit the huge potential underlying these pre-trained models.