Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

Yi-Min Chou, Yi-Ming Chan, Jia-Hong Lee, Chih-Yi Chiu, Chu-Song Chen

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 2049-2056. https://doi.org/10.24963/ijcai.2018/283

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.
Keywords:
Machine Learning: Neural Networks
Machine Learning: Deep Learning
Computer Vision: Computer Vision