Unsupervised Representation Learning of Image-Based Plant Disease with Deep Convolutional Generative Adversarial Networks

Li, Jie; Jia, Junjie; Xu, Donglai

doi:10.23919/chicc.2018.8482813

Cited by 101 publications

(35 citation statements)

References 10 publications

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“…In general, GAN series models are divided into two categories: conditional and unconditional GANs. The Wasserstein-GAN [48]- [50], DC-GAN [51], etc. are outstanding examples of unconditional GANs, which are committed to producing realistic and high-resolution image data.…”

Section: E Recurrent Neural Networkmentioning

confidence: 99%

Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Zhang

Xiang

et al. 2020

IEEE Access

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This paper reviews studies on neural networks in aerodynamic data modeling. In this paper, we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced-order models (ROMs). Subsequently, the history and fundamental methodologies of neural networks are introduced. Furthermore, we classify the neural networks based studies in aerodynamic data modeling and illustrate comparisons among them. These studies demonstrate that neural networks are effective approaches to aerodynamic data modeling. Finally, we identify three important trends for future studies in aerodynamic data modeling: a) the transformation method and physics informed models will be combined to solve high-dimensional partial differential equations; b) in the research area of steady aerodynamic response predictions, model-oriented studies and data-integration-oriented studies will become the future research directions, while in unsteady aerodynamic response predictions, radial basis function neural networks (RBFNNs) are the best tools for capturing the nonlinear characteristics of flow data, and convolutional neural networks (CNNs) are expected to replace long short-term memories (LSTMs) to capture the temporal characteristics of flow data; and c) in the field of steady or unsteady flow field reconstructions, the CNN-based conditional generative adversarial networks (cGANs) will be the best frameworks in which to discover the spatiotemporal distribution of flow field data. INDEX TERMS Aerodynamics, convolutional neural networks, neural networks, generative adversarial networks, recurrent neural networks.

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Section: E Recurrent Neural Networkmentioning

confidence: 99%

Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Zhang

Xiang

et al. 2020

IEEE Access

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“…At present, as a widely used model and branch of deep neural network, convolution neural network has strong processing and representation ability for complex data, which can effectively extract robust and invariant features from big data, and then is conducive to the subsequent image classification. Therefore, this paper uses the combination of convolution neural network and antagonism neural network to generate antagonism network Deep Convolutional Generative Adversarial Networks (DCGAN) [34]. DCGAN is to replace generator and discriminator in antagonism neural network with convolution neural network.…”

Section: B Network Structurementioning

confidence: 99%

“…The network diagram of generator g in DCGAN is shown in Figure 2 below: FIGURE 2. Generator network structure [34].…”

Section: B Network Structurementioning

confidence: 99%

Research on Medical Image Classification Based on Machine Learning

Tang

2020

IEEE Access

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In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. Because the deep neural network needs a large number of labeled samples to complete the training, and the cost of medical image labeling is very high, the training samples needed to train the deep neural network are insufficient. In this paper, a semi supervised learning based image classification method is proposed, which uses a small amount of labeled pathological image data to train the network model, and then integrates the features extracted by the network to classify the image. The results show that the classification effect of the neural network is better than convolution neural network and other traditional image classification models. To some extent, it can reduce the dependence of neural network on a large number of training samples, and effectively reduce the over fitting phenomenon of the network. Through the analysis of the overall classification accuracy and kappa coefficient of different classification methods under different sample numbers, it is found that the overall classification accuracy and kappa coefficient are increasing with the increasing number of training samples. Especially in the case of a small number of training samples, compared with other deep neural networks and traditional classification methods, the classification accuracy of the counter neural network is about 10% higher than that of other neural networks and traditional classification methods, and the advantages are more obvious. INDEX TERMS Generative adversarial network, deep learning, feature extraction, image classification.

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“…In this section, we describe the internal architecture of our interactive image editing system, shown in Figure 1 (right), composed of image editing models based on Deep Convolutional Generative Adversarial Networks (DCGAN) [8]. We use two image editing models: a model without a generation constraint and a model with a generation constraint.…”

Section: Dcgan-based Image Editing Models and Dialogue Strategymentioning

confidence: 99%

An Interactive Image Editing System Using an Uncertainty-Based Confirmation Strategy

et al. 2020

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We propose an interactive image editing system that has a confirmation dialogue strategy using an entropy-based uncertainty calculation on its generated images with Deep Convolutional Generative Adversarial Networks (DCGAN). DCGAN is an image generative model that learns an image manifold of a given dataset and enables continuous change of an image. Our proposed image editing system combines DCGAN with a natural language interface that accepts image editing requests in natural language. Although such a system is helpful for human users, it often faces uncertain requests to generate acceptable images. A promising approach to solve this problem is introducing a dialogue process that shows multiple candidates and confirms the user's intention. However, confirming every editing request creates redundant dialogues. To achieve more efficient dialogues, we propose an entropy-based dialogue strategy that decides when the system should confirm, and enables effective image editing through a dialogue that reduces redundant confirmations. We conducted image editing dialogue experiments using an avatar face illustration dataset for editing by natural language requests. Through quantitative and qualitative analysis, our results show that our entropy-based confirmation strategy achieved an effective dialogue by generating images desired by users. INDEX TERMS Confirmation, generative adversarial networks, image editing, natural language interface.

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Unsupervised Representation Learning of Image-Based Plant Disease with Deep Convolutional Generative Adversarial Networks

Cited by 101 publications

References 10 publications

Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Research on Medical Image Classification Based on Machine Learning

An Interactive Image Editing System Using an Uncertainty-Based Confirmation Strategy

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