Abstract
Deep convolutional neural networks (CNNs) have shown impressive performance for image recognition when trained over large scale datasets such as ImageNet. CNNs can extract hierarchical features layer by layer starting from raw pixel values, and representations from the highest layers can be efficiently adapted to other visual recognition tasks. In this paper, we propose heterogeneous deep convolutional neural networks (HCNNs) to learn features from different CNN models. Features obtained from heterogeneous CNNs have different characteristics since each network has a different architecture with different depth and the design of receptive fields. HCNNs use a combination network (i.e. another multi-layer neural network) to learn higher level features combining those obtained from heterogeneous base neural networks. The combination network is also trained and thus can better integrate features obtained from heterogeneous base networks. To better understand the combination mechanism, we backpropagate the optimal output and evaluate how the network selects features from each model. The results show that the combination network can automatically leverage the different descriptive abilities of the original models, achieving comparable performance on many challenging benchmarks.
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Acknowledgements
This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2012CB316400, the National Natural Science Foundation of China under Grant Nos. 61532018, 61322212 and 61550110505, the National High Technology Research and Development 863 Program of China under Grant No. 2014AA015202, Beijing Science And Technology Project under Grant No. D161100001816001. This work is also funded by Lenovo Outstanding Young Scientists Program (LOYS).
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Li, X., Herranz, L., Jiang, S. (2016). Heterogeneous Convolutional Neural Networks for Visual Recognition. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_26
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