The invention discloses a generative-adversarial-network-based blurred face reconstruction method and system. The face reconstruction method comprises: acquiring a training sample and to-be-re-reconstructed data; carrying out generative adversarial network training on two convolutional neural networks by the training sample to obtain an optimized-target-based generative network model; and acquiring a blurred face from the to-be-re-reconstructed data, inputting the blurred face into the generative network model to carry out de-blurring reconstruction of the face, thereby obtaining a clear face.The face reconstruction method and the system have the following beneficial effects: a phenomenon that the image obtained by using an interpolation method is too smooth is avoided; the image recovered by the model becomes clear and more high-frequency details are provided; because no frequency domain method is needed, a phenomenon that the frequency-domain data are lack of correlation is avoided;and the blurred face or shielded face can be reconstructed rapidly and the definition and similarity are high. In addition, the face reconstruction system is composed of an acquisition unit, a modelgeneration unit, and a face reconstruction unit; and the face reconstruction system has the same beneficial effects as the face reconstruction method.

Generative-adversarial-network-based blurred face reconstruction method and system

The invention discloses a WGAN model method based on a depth convolution nerve network, and belongs to the deep learning nerve network field; the method comprises the following steps: S1, forming a Wasserstein generative confrontation network WGAN model; S2, forming a discriminator with the depth convolution nerve network structure; S3, forming a generator with a transposition convolution nerve network structure; S4, employing a Wasserstein distance loss function as a discriminator loss function; S5, preparing a data set so as to train the formed network. The method creatively proposes the depth convolution nerve network and WGAN combined forming mode according to the generative confrontation network model features, forms the generator and the discriminator as the depth convolution nerve network mode, and uses the WGAN loss function mode; the method can learn image features in the training process, and can use the loss functions to reflect the generative image quality.

WGAN model method based on depth convolution nerve network

The invention relates to an image single classification method based on a generative confrontation network. The image single classification method based on a generative confrontation network includesthe steps: constructing a generator in the generative confrontation network by means of a dense connection block structure; constructing a discriminator in the generative confrontation network; inputting positive sample training data, and using a gradient punishment algorithm to train the generative confrontation network; according to the classification effect of the model on the verification setduring the training process, adjusting the network parameters, and using the Early Stopping strategy to find the classification optimal iteration number of the model; and after the model training is completed, using the discriminator in the generative confrontation network to test the test set data, and determining the classification effect of the model through a classification recall index CRI determination model. The image single classification method based on a generative confrontation network can automatically generate a negative sample set, and can solve the problem that in a current single classification method, artificial construction of a negative sample data set is likely to cause over-fitting of the classifier.

Image single classification method based on generative confrontation network

The invention discloses a data sample tag processing method and apparatus. The method comprises: an indeterminate tag sample set and a determined tag sample set are obtained; iterative processing is carried out by the following steps until the learning tag accuracy reaches a preset requirement: the current indeterminate tag sample set and determined tag sample set are combined and training is carried out to obtain a prediction model; indeterminate tag samples are inputted into the prediction model an prediction value outputted by the model is determined as a learning tag of indeterminate tag samples; a sampling sub set with a learning tag is obtained and the learning tag of the sampling sub set is checked to obtain an accuracy rate of the learning rate; if the accuracy rate of the learningtag does not reach the preset requirement, the tag checking result is determined as a marking result of the sampling sub set and the sampling set is transferred from the indeterminate tag sample setto the determined tag sample set; and after ending of iteration, the current learning tag of the indeterminate tag sample set is determined as a marking result.

Data sample tag processing method and apparatus

The invention relates to a method comprising receiving a set of input samples, said set of input images comprising real images and generated images; extracting a set of feature maps from multiple layers of a pre-trained neural network for both the real images and the generated images; determining statistics for each feature map of the set of feature maps; comparing statistics of the feature maps for the real images to statistics of the feature maps for the generated images by using a distance function to obtain a vector of distances; and averaging the distances of the vector of distances to have a value indicating a diversity of the generated images. The invention also relates to technical equipment for implementing the method.

Method and an apparatus for evaluating generative machine learning model

The invention relates to a pest and disease image generation method based on a generative adversarial network In the prior art, sampling images of pest and disease images are less By using the method of the invention, the above defect is overcome The method comprises the following steps of collecting and preprocessing trained images; based on a deep-convolution neural network model, constructing a discrimination network and a generation network; training the discrimination network and the generation network; and according to the trained generation network, generating the pest and disease images In the invention, according to a few of existing pest and disease images, a lot of pest and disease images which are similar to a reality are generated, a sample image is provided for pest and disease image identification, and problems that the pest and disease images in an actual field are less and acquisition cost is high are solved

Pest and disease image generation method based on generative adversarial network

The invention relates to a method of disease image identification based on hybrid convolutional neural network fused with context information, which solves the defects of low image identification rate and poor robustness as compared with the prior art. The method comprises the steps of collecting and preprocessing training images; constructing and training a hybrid convolutional neural network model; collecting and preprocessing images under test; and inputting a test sample into the hybrid convolutional neural network model after the training is completed, conducting automatic identification of disease images, and identifying the type of the images under test. The method of the invention combines the image information of the disease images and the context information to construct the hybrid convolutional neural network, thus improving the accuracy of the disease identification and enhancing the robustness of the disease identification algorithm.

Method of disease image identification based on hybrid convolutional neural network fused with context information

The invention relates to an antagonistic characteristic learning-based paddy rice aphid detection method. Compared with the prior art, the defects of low paddy rice aphid identification rate and poor robustness are overcome. The method comprises the following steps of collecting and preprocessing a paddy rice aphid image; obtaining a paddy rice aphid image detection model; collecting and preprocessing a to-be-detected paddy rice image; and marking a specific position of an aphid in the image: inputting the to-be-detected image to the trained paddy rice aphid image detection model for performing paddy rice aphid detection, and locating and marking the specific position of the aphid in the image. Through antagonistic training between an image judgment network model and an image generation network model, the identification capability of the image judgment network model is improved and the paddy rice aphid identification rate is increased.

Antagonistic characteristic learning-based paddy rice aphid detection method

The invention relates to a pest image identification method based on a multi-space convolution neural network, and solves the shortcomings of low image identification rate and poor robustness compared with the prior art. The method comprises the following steps of collecting and pre-processing training images, constructing a multi-scale MS-CNN network model and a multi-core classification model, collecting and pre-processing image to be tested, taking a test sample as input and training the multi-core model and the MS-CNN model, and conducting automatic identification of pest images after the training. According to the pest image identification method based on the multi-space convolution neural network, the accuracy rate of pest identification is improved, the robustness of a pest identification algorithm is enhanced and the practical application level is achieved.

Pest image identification method based on multi-space convolution neural network

The invention relates to a disease image identification method suitable for multi-size picture information, and overcomes the defects of low identification rate and poor robustness due to that the resolutions and sizes of disease images are different as compared with the prior art. The method of the invention includes the steps of collecting and pre-processing training images; constructing and training an MCNN network model; collecting and pre-processing images under test, shooting disease images to be identified using an acquisition device, and performing histogram equalization of the disease images under test to obtain a test sample; and inputting the test sample into the trained MCNN network model for automatic identification of the disease images. The method of the invention can directly extract features of the images with different resolutions and sizes, without unifying the size of the images before the identification, and perform classification prediction.

Wang Rujing

Papers

Pest and disease image generation method based on generative adversarial network

Method of disease image identification based on hybrid convolutional neural network fused with context information

Antagonistic characteristic learning-based paddy rice aphid detection method

Pest image identification method based on multi-space convolution neural network

Disease image identification method suitable for multi-dimensional picture information