Jiang Kan, You Jie, Dorj Ulzii-Orshikh, Kim Hyongsuk, Lee Joonwhoan
Department of Computer Science and Engineering, Artificial Intelligence Lab, Jeonbuk National University, Jeonju, South Korea.
Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South Korea.
Front Plant Sci. 2022 Sep 15;13:989086. doi: 10.3389/fpls.2022.989086. eCollection 2022.
For continual learning in the process of plant disease recognition it is necessary to first distinguish between unknown diseases from those of known diseases. This paper deals with two different but related deep learning techniques for the detection of unknown plant diseases; Open Set Recognition (OSR) and Out-of-Distribution (OoD) detection. Despite the significant progress in OSR, it is still premature to apply it to fine-grained recognition tasks without outlier exposure that a certain part of OoD data (also called known unknowns) are prepared for training. On the other hand, OoD detection requires intentionally prepared outlier data during training. This paper analyzes two-head network included in OoD detection models, and semi-supervised OpenMatch associated with OSR technology, which explicitly and implicitly assume outlier exposure, respectively. For the experiment, we built an image dataset of eight strawberry diseases. In general, a two-head network and OpenMatch cannot be compared due to different training settings. In our experiment, we changed their training procedures to make them similar for comparison and show that modified training procedures resulted in reasonable performance, including more than 90% accuracy for strawberry disease classification as well as detection of unknown diseases. Accurate detection of unknown diseases is an important prerequisite for continued learning.
为了在植物病害识别过程中持续学习，首先有必要区分未知病害和已知病害。本文探讨了两种不同但相关的深度学习技术，用于检测未知植物病害：开放集识别（OSR）和分布外（OoD）检测。尽管OSR取得了显著进展，但在没有异常值暴露的情况下将其应用于细粒度识别任务仍为时过早，即需要准备一部分OoD数据（也称为已知未知）用于训练。另一方面，OoD检测需要在训练期间有意准备异常值数据。本文分析了OoD检测模型中包含的双头网络，以及与OSR技术相关的半监督OpenMatch，它们分别显式和隐式地假设了异常值暴露。为了进行实验，我们构建了一个包含八种草莓病害的图像数据集。一般来说，由于训练设置不同，无法对双头网络和OpenMatch进行比较。在我们的实验中，我们改变了它们的训练过程，使其相似以便进行比较，并表明修改后的训练过程产生了合理的性能，包括草莓病害分类的准确率超过90%以及未知病害的检测。准确检测未知病害是持续学习的重要前提。
