鉴别器
人工智能
自编码
水准点(测量)
零(语言学)
模式识别(心理学)
编码器
计算机科学
数学
机器学习
深度学习
统计
地理
大地测量学
哲学
探测器
电信
语言学
作者
Fangming Zhong,Zhikui Chen,Yuchun Zhang,Feng Xia
标识
DOI:10.1016/j.compag.2020.105828
摘要
Plant diseases can cause significant production and economic losses, and also seriously restrict the sustainable development of agriculture. Traditional plant diseases recognition method is time-consuming and highly dependent on expert experience. Therefore, most of the existing works design models based on deep learning to automatic recognition. However, they are sample-intensive and hard for the diagnosis of some Citrus aurantium L. diseases with only a few or even zero labeled samples for training. In this paper, we propose a novel generative model for zero- and few-shot recognition of Citrus aurantium L. diseases using conditional adversarial autoencoders (CAAE). CAAE learns to synthesize visual features so that the zero- and few-shot recognition can be transformed to a conventional supervised classification problem. Specifically, CAAE consists of encoder, decoder, and discriminator. Different from conditional variational autoencoder (CVAE), we impose a discriminator to train the encoder by adversarially minimizing the loss between the prior distribution and the encoding distribution. Our model achieves a harmonic mean accuracy of 53.4% for zero-shot recognition of Citrus aurantium L. diseases, which is 50.4% higher than CVAE. Extensive experiments carried out on public zero-shot benchmark datasets and a further case study on our own collected dataset of Citrus aurantium L. diseases demonstrate that our model is suitable for the application of zero- and few-shot Citrus aurantium L. diseases diagnosis.
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