Abstract
Fusing information extracted from multiple layers of a convolutional neural network has been proven effective in several domains. Common fusion techniques include feature concatenation and Fisher embedding. In this work, we propose to fuse multilayer information by genetic programming (GP). With the evolutionary strategy, we iteratively fuse multilayer information in a systematic manner. In the evaluation, we verify the effectiveness of discovered GP-based representations on three image classification datasets, and discuss characteristics of the GP process. This study is one of the few works to fuse multilayer information based on an evolutionary strategy. The reported preliminary results not only demonstrate the potential of the GP fusion scheme, but also inspire future study in several aspects.
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Acknowledgement
This work was partially supported by the Ministry of Science and Technology under the grant 107-2221-E-194-038-MY2 and 107-2218-E-002-054, and the Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
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Chu, WT., Chu, HA. (2019). A Genetic Programming Approach to Integrate Multilayer CNN Features for Image Classification. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_53
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DOI: https://doi.org/10.1007/978-3-030-05710-7_53
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