Rectified Meta-learning from Noisy Labels for Robust Image-based Plant Disease Classification
Deming Zhai, Ruifeng Shi, Junjun Jiang, Xianming Liu
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 inevitably introduce 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.