Nonlinear Multi-Model Reuse

The goal of model reuse is to build a model in a new target domain by reusing some pre-trained source models. It can significantly reduce the training costs and the data required for training, and hence has various potential applications. Most of the existing model reuse approaches only reuse the output features or labels of the source model, and more information contained in the model are ignored. Besides, only a single model can be utilized in these approaches. A recently proposed multi-model reuse method is able to remedy these drawbacks by utilizing the hidden layer representations of multiple source models to help improve the representations in the target model, but it assumes that there are linear connections between the source and target models. This assumption is too restrictive and may be not valid in real-world applications. In this paper, we relax this assumption by introducing the manifold regularization scheme to exploit arbitrary nonlinear relationships between the source and target models. Effectiveness of our method is demonstrated empirically by the extensive experiments in the popular person re-identification task for smart city application.