APPLICATION OF METRIC LEARNING IN AGRICULTURE
DOI:
https://doi.org/10.24867/21BE18TumbasKeywords:
metric learning, machine learningAbstract
In this paper, we show the application of metric learning and ability to easily scale to new classes without the need for retraining in the field of agriculture, on the classification of flower types, plant leaf types and plant leaf diseases.
References
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Deep Residual Learning for Image Recognition, Microsoft Research, arXiv:1512.03385, 2015.
[2] Leslie N. Smith: Cyclical Learning Rates for Training Neural Networks, U.S. Naval Research Laboratory, arXiv: 1506.01186, 2017.
[3] Xun Wang, Xintong Han, Weilin Huang∗, Dengke Dong, Matthew R. Scott: Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning, Malong Technologies, Shenzhen, China, arXiv: 1904.06627, 2022
[4] Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart J. Russell: Distance Metric Learning with Application to Clustering with Side-Information, Advances in Neural Information Processing Systems (NIPS) 15, pages 505–512, 2002.
[2] Leslie N. Smith: Cyclical Learning Rates for Training Neural Networks, U.S. Naval Research Laboratory, arXiv: 1506.01186, 2017.
[3] Xun Wang, Xintong Han, Weilin Huang∗, Dengke Dong, Matthew R. Scott: Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning, Malong Technologies, Shenzhen, China, arXiv: 1904.06627, 2022
[4] Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart J. Russell: Distance Metric Learning with Application to Clustering with Side-Information, Advances in Neural Information Processing Systems (NIPS) 15, pages 505–512, 2002.
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Published
2023-01-08
Issue
Section
Electrotechnical and Computer Engineering
