Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis

• URL: http://arxiv.org/abs/2003.07603v2
• Date: Wed, 18 Mar 2020 03:01:25 GMT
• Title: Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis
• Authors: Ruifeng Shi, Deming Zhai, Xianming Liu, Junjun Jiang, Wen Gao
• Abstract summary: Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
• Score: 64.82680813427054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Plant diseases serve as one of main threats to food security and crop production. It is thus valuable to exploit recent advances of artificial intelligence to assist plant disease diagnosis. One popular approach is to transform this problem as a leaf image classification task, which can be then addressed by the powerful convolutional neural networks (CNNs). However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation. To overcome this problem, we propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information. The proposed method enjoys the following merits: i) A rectified meta-learning is designed to pay more attention to unbiased samples, leading to accelerated convergence and improved classification accuracy. ii) Our method is free on assumption of label noise distribution, which works well on various kinds of noise. iii) Our method serves as a plug-and-play module, which can be embedded into any deep models optimized by gradient descent based method. Extensive experiments are conducted to demonstrate the superior performance of our algorithm over the state-of-the-arts.

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