31results about How to "Enrich training data" patented technology

The invention discloses a method for improving the detection precision of ships approaching a shore in an SAR image. The method is based on a deep learning theory, and mainly comprises four parts: a generative adversarial network, K-means clustering, scene amplification, and a classic detection network (Faster R-CNN, Cascade R-CNN, SSD, and RetinaNet). The generative adversarial network realizes image feature extraction, the K-means clustering method realizes image dichotomy by using extracted features to obtain a classification result of each image, scene amplification is performed to obtaina more balanced data set, and the classical detection network performs training by using the processed data set and executes a detection task. While the detection precision of the offshore ship is slightly improved, the detection precision of the ship approaching the shore on the Faster R-CNN, the Cascade R-CNN, the SSD and the RetinaNet network is improved by 8.60%, 8.32%, 18.15% and 12.40% respectively, and the detection precision of the ship approaching the shore is improved.

The embodiment of the invention provides a method and a device for predicting a service index executed by a computer. The method comprises: first, obtaining dataset sequences, wherein the data set sequence comprises m data sets which correspond to m continuous time periods and are arranged according to a time sequence, and the ith data set in the m data sets comprises entity characteristics of a to-be-tested service entity in the ith time period and respective index values of a plurality of to-be-tested service indexes of the to-be-tested service entity; inputting the data set sequence into apre-trained recurrent neural network to obtain an output result. Therefore, according to the output result, the index values of the plurality of to-be-tested service indexes of the to-be-tested service entity in the next time period of the mth time period can be determined.

The invention discloses a method and system for classification modeling based on protein length and DCNN, and belongs to the field of protein prediction and analysis. The technical problem to be solved is how to use deep learning to predict and analyze the secondary structure of the protein and improve the accuracy. The method comprises the following steps: taking a plurality of big data sets as atraining set, extracting PSSM features generated by PSI-Blast in the data sets, and performing format conversion on the PSSM features through a sliding window; grouping proteins in the training set based on the protein length to obtain a plurality of model groups; and constructing a predication model corresponding to every model group based on a deep convolutional network, and training the prediction models by the model groups to obtain trained prediction models. The system includes an input module, a format conversion module, a grouping module and a model training module.

The invention discloses a semi-supervised learning pseudo label assignment method based on clustering fusion, and the method comprises the steps: carrying out the semi-supervised learning of a convolutional neural network with a label-free data set, carrying out the pre-training of the neural network through employing labeled data and label-free data, and extracting data features through employinga trained network; assigning pseudo tags to N pieces of untagged data closest to the tagged data by using a nearest neighbor method; analyzing all the data information by using k-means clustering, and endowing clustered pseudo tags to the data which is not tagged; and continuously training the convolutional neural network by using the obtained label data and pseudo label data to obtain an optimalnetwork for label assignment. The method can be suitable for semi-supervised learning under deep learning in various fields; information of label-free data can be fully mined, and training data withricher content are provided for a network; the principle is clear and easy to understand, and codes are easy to implement.

The invention discloses a multi-commodity image synthesis method and device, electronic equipment and a storage medium. The method relates to the field of deep learning, and can comprise the steps: collecting a commodity image of a commodity for any commodity, carrying out the main body detection of the commodity image, and determining a binary segmentation image corresponding to the commodity according to a main body detection result and a constructed collection scene background image; and when a multi-commodity image needs to be synthesized, obtaining an application scene background image, synthesizing the at least two binary segmentation images to the application scene background image, and performing assignment on pixel points in the binary segmentation images according to the commodity images corresponding to the binary segmentation images and corresponding pixel point values in the application scene background image. By applying the scheme of the invention, the labor cost can besaved, and the processing efficiency can be improved.

The invention discloses a rapid image storage method and system suitable for an autonomous driving simulation platform. The rapid image storage method comprises the steps that an image stored in a video memory is directly sent into a computer memory; according to the invention, high-speed lossless screenshot can be realized on the autonomous driving simulation platform, the universality of the autonomous driving simulation platform is improved, and more and more accurate training data can be provided for deep learning algorithm development.

The invention relates to a pedestrian re-identification network training method based on branch learning and layered pseudo labels, wherein a pedestrian re-identification network is a mutual average teaching network. The training method comprises the following steps: obtaining a label data set and a label-free data set, taking the label data set as one layer, dividing the label-free data set into N layers, and assigning the pseudo label to the label-free data of each layer to form N layers of pseudo label data, wherein N is a constant; constructing a branch learning framework comprising N+1 mutual mean-teaching network branches sharing weights, wherein one branch is used for inputting label data for training, and the N layers of pseudo label data are correspondingly input into the other N branches for training; and constructing a loss function of each branch, determining a total loss function of the branch learning framework, performing multiple rounds of training based on the total loss function, and re-layering the label-free data set in each round of training process. Compared with the prior art, the network trained by the invention is more accurate, and the convergence speed of the network during training is high.

The invention discloses a enterprise credit extension model construction method, device and equipment and a readable storage medium. The construction method comprises the following steps: constructinga tax payment data prediction model in advance; obtaining industrial and commercial data of an enterprise, and inputting the industrial and commercial data into the tax payment data prediction modelto obtain tax payment data corresponding to the industrial and commercial data; and based on the industrial and commercial data, the derivative data corresponding to the industrial and commercial dataand the tax payment data, constructing an enterprise credit extension model, so as to predict the credit risk of the target customer based on the enterprise credit extension model after the enterprise credit extension model is constructed. According to the invention, the credit risk of the enterprise can be assessed more comprehensively, so that the result of predicting the credit risk of the target customer is more reliable, and the technical problem that the risk level of the enterprise cannot be assessed comprehensively and multi-dimensionally by the existing enterprise credit scoring technology is solved.

The invention provides an image processing method, device and equipment and a computer readable storage medium, and the method comprises the steps: obtaining a to-be-corrected image collected by an image collection device, wherein the to-be-corrected image is a driver face image with a face rotating by a preset first angle, and the first angle is determined according to an included angle between the image collection device and a driver; taking the first angle as a constraint condition of the to-be-corrected image, and inputting the to-be-corrected image and the first angle into a preset conditional adversarial network to obtain a corrected target image, the target image being a front face image of the driver; and according to the target image and a preset face recognition technology, carrying out recognition operation on the identity information of the driver. Different from the existing technical scheme of labeling the face feature information in the image, the first angle is used asthe constraint condition, and the training data is relatively rich. In the process of face recognition based on a target image, the target image is the front face image of a driver, so that the recognition precision is improved.

The invention discloses an unsupervised representation learning method and device based on multi-source heterogeneous features, and the method comprises the steps: extracting and integrating multi-source heterogeneous data, designing and training a first encoder of structured data features, and carrying out the coding of the structured data through the first encoder, and obtaining a first coding result; designing and training a second encoder with unstructured data features, and encoding the unstructured data by using the second encoder to obtain a second encoding result; processing the first coding result and the second coding result to obtain a multi-source heterogeneous fusion feature for describing the instance; designing a DNN network based on the multi-source heterogeneous fusion features, and training to obtain feature representation vectors of the multi-source heterogeneous fusion features projected to a multi-dimensional space; and performing the tasks of similarity matching, classification and clustering among the instances by using the feature representation vectors. On the basis of an unsupervised condition, discriminative representation learning of an instance level is realized; and more training data and a better network structure are provided.

The embodiment of the invention provides an image processing method and device and a storage medium. The method comprises the steps of obtaining a first image which comprises at least one time domain feature and at least one frequency domain feature and is obtained according to first voice data to be processed, and updating the first image according to a preset strategy to obtain a second image, wherein the preset strategy at least comprises one of the following items: transforming a time domain position of at least one time domain feature on a time domain, or transforming the frequency domain position of at least one frequency domain feature on the frequency domain. According to the scheme, rich and diversified training data can be provided for the training model, so that the training effect is improved.

A business document-driven intelligent customer service construction method is characterized by comprising the following steps: step 1, collecting a current input q of a user, a business document D and a dialogue history, and encoding and pooling the current input q, the business document D and the dialogue history to obtain a sentence level representation of the current input q, the business document D and the dialogue history; 2, taking each item in the sentence level representation Ds of the business document as a discourse unit, and sequentially establishing a correlation between the sentence level representation q currently input by the user and each discourse unit and a correlation between each discourse unit and the sentence level representation Hs of the dialogue history, therefore, document structure analysis and dialogue state tracking are realized in sequence; and step 3, classifying the business document and the dialogue history based on the association relationship obtained in the step 2, and reasoning the current answer to generate an optimal strategy. Corpus association and accurate reasoning are realized by referring to a rhetorical structure theory, the problem of an information slot is overcome, system dialogue is represented by an interaction state of a discourse unit, and the system dialogue efficiency is improved. And the conversation is smoothly driven.