One-vs-One classification for deep neural networks

作者：

Highlights：

• A novel One-vs-One classification method for deep neural networks is introduced.

• The One-vs-One scheme is compared to the standard One-vs-All scheme on four datasets.

• The results of two convolutional neural networks are better with the proposed method.

• When fine-tuning pre-trained architectures, the One-vs-All method performs best.

摘要

•A novel One-vs-One classification method for deep neural networks is introduced.•The One-vs-One scheme is compared to the standard One-vs-All scheme on four datasets.•The results of two convolutional neural networks are better with the proposed method.•When fine-tuning pre-trained architectures, the One-vs-All method performs best.

论文关键词：Deep learning,Computer vision,Multi-class classification,One-vs-One classification,Plant recognition

论文评审过程：Received 17 October 2019, Revised 19 June 2020, Accepted 30 June 2020, Available online 1 July 2020, Version of Record 11 July 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107528