Abstract
Object classification is a classic problem in the field of pattern recognition. The traditional deep neural networks have been able to achieve good results on some classification problems, however, there are still many difficulties to be overcome in the fine-grained identification task, whose performance are still baffled by practical problems. In this paper, we introduce neural architecture search (NAS) to search the appropriate network according to the specific data set, which do not need more engineering work to adjust parameters for the optimized performance. We further combine the Destruction and Construction Learning (DCL) network and the NAS-based network for pollen recognition. To this end, we use a fusion algorithm to implement the combination of different networks, and won the pollen recognition competition held at the international pattern recognition Conference (ICPR) 2020.
C. Fang and Y. Hu—Contribute equally.
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Acknowledgement
The work was supported by the Natural Science Foundation of China (62076016, 61672079). Baochang Zhang is also with Shenzhen Academy of Aerospace Technology, Shenzhen, China, and he is corresponding author. He is in part supported by Shenzhen Science and Technology Program (No. KQTD20161125151346).
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Fang, C., Hu, Y., Zhang, B., Doermann, D. (2021). The Fusion of Neural Architecture Search and Destruction and Construction Learning. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_35
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