31results about How to "Reduce the amount of labeling" patented technology

The invention provides a method for training a convolution neural network through a small-scale face data set. The method is characterized by comprising the following steps: step one, using an originally marked face sample set to train a VGG face recognition model of the convolution neural network; step two, constructing a deep convolution to generate a DCGAN model of a confrontation network, andusing the originally marked face sample set to train the deep convolution so as to generate the confrontation network; step three, generating an unlabelled face sample set through DCGAN; step four, generating a face data set mark for the DCGAN; step five, using the originally marked face sample set to train a plug-and-play generated network PPGN; step six, generating a labeled face sample set through the PPGN; step seven, training the convolution neural network in combination with a DCGAN and PPGN generated sample set and the originally marked sample set; step eight, repeatedly training, namely repeating the step four, step five, step six and step seven repeatedly; and step nine, using the originally marked face sample set to finely adjust the VGG network.

The invention provides a text sentiment classification method and device. A text coding model belonging to the same field as a to-be-classified text is used for analyzing context semantic information in the to-be-classified text in an omni-directional mode to obtain a corresponding text vector; meanwhile, a position vector of the sentiment classification attribute word is acquired, and then the text vector and the position vector are spliced to obtain a text and a position vector. The text and the position vector contain context semantic information of to-be-classified text and position information of the emotion classification attribute words, and the target emotion classification model can define an emotion analysis object according to the position information of the emotion classification attribute words, so that the accuracy of an emotion analysis result is improved. Moreover, the text vector can more accurately represent the semantic information of the text to be classified, so that the emotion classification model can better understand the context information of the text, and the classification accuracy is further improved. In addition, the data annotation quantity can be greatly reduced by adopting the pre-trained text coding model.

The invention provides an image annotation method and device, an image semantic segmentation method and device, and a model training method and device, relates to the technical field of artificial intelligence, and is used for improving the sample image annotation efficiency. According to the image annotation method, the edge pixel points in the sample image are detected, the target image blocks in the image blocks in the sample image are screened according to the edge pixel points, and the target image blocks are annotated, so that the annotation result of the sample image is obtained, and due to the fact that all the pixel points in the sample image do not need to be annotated, the annotation quantity in the sample annotation process can be relatively reduced, and the efficiency of annotating the sample image is improved; and as the image has certain redundant information, the accuracy of the image semantic segmentation model is not influenced even if all pixel points in the sample image are not annotated and the image semantic segmentation model is trained.

The invention relates to an image processing method, an image processing device and a storage medium. The method comprises the steps of obtaining a to-be-processed image; selecting at least one category based on category information output by the to-be-processed image through the image classifier, and determining a thermodynamic diagram of each category in the at least one category based on the category information; for each category of thermodynamic diagrams in the at least one category of thermodynamic diagrams, determining a first positioning frame set corresponding to the target object inthe to-be-processed image; determining a second positioning frame set of the to-be-processed image according to an unsupervised target detection algorithm; and determining a target positioning frame set in the to-be-processed image according to the first positioning frame set and the second positioning frame set, the target positioning frame being used for representing the position of the target object in the to-be-processed image. Through the technical scheme of the invention, the target detection algorithm and the deep learning algorithm are combined, the data acquisition difficulty is low,the data annotation amount is small, and the position of the target object in the to-be-processed image can be quickly and accurately determined.

The invention discloses a model building method and system, a paragraph label obtaining method and a medium, and relates to the field of natural language processing transfer learning, and the method comprises the steps: collecting all judgment document data from a database, and obtaining pre-training data; defining paragraph labels of different types of judgment documents; marking paragraph labels of different types of judgment documents to obtain training data; constructing a judgment document structured model; pre-training the model; training a pre-trained judgment document structured model by utilizing the training data; and debugging the trained judgment document structured model to obtain a final judgment document structured model, wherein the input of the judgment document structured model is judgment document text data, a task prefix is added to a paragraph of the input judgment document, and the output of the judgment document structured model is paragraph label text data of the judgment document. The model established by adopting the method can predict any type of judgment document paragraph label after being trained.

The present invention discloses a self-learning face verification method. The method comprises the steps of: the step 101: employing a marked face sample set L to train a convolutional neural network;the step 102: employing the convolutional neural network to perform marking of an unmarked sample set U; the step 103: employing the sample set U to perform fine tuning of the convolutional neural network; the step 104: employing the sample set L to perform fine tuning of the convolutional neural network; and the step 105: repeatedly performing the steps 102, 103 and 104 for many times.

The invention discloses a repeated incoming call preprocessing method, device and equipment based on 95598 and a storage medium. The processing method comprises the following steps: S101, data acquisition; s102, removing business data from the obtained data to obtain screened data; s103, labeling and primary screening are carried out; s104, analyzing the primarily screened data by using text similarity, judging whether the primarily screened data are the same appeal event or not, and if so, inputting the text content of the repeated incoming call corresponding to the primarily selected data into an algorithm unit; if not, sending the primarily selected data to a processing end; s105, the algorithm unit judges whether the received text content is similar or not, if yes, it is considered that the corresponding appeal event is a repeated call, and the repeated call is output; and if not, sending the corresponding text content to the processing end in the step S104. According to the method, the neural network data annotation amount is reduced, the training period is shortened, the text analysis accuracy can be improved, manpower is reduced, and the early-stage preparation time is shortened.

The embodiment of the invention provides a neural language network model training method and device, equipment and a storage medium, which are used for reducing the labeling amount of training sample data and improving the training efficiency of a language model. The method comprises the following steps: acquiring training sample data; cyclically executing the following steps until the neural language network model obtained by training meets a preset requirement: predicting the unlabeled training sample data by using the neural language network model obtained by previous training, and determining an identification probability for representing that each training sample data is identified; according to a preset selection strategy, on the basis of the recognition probability of each training sample data, selecting a part of training sample data requests from the training sample data which are not labeled for manual labeling; and obtaining the manually labeled training sample data, and training the neural language network model obtained by the previous training based on the manually labeled training sample data to obtain a new neural language network model.

The invention provides a text entity relationship recognition method, a text entity relationship recognition system and a medium. The text entity relationship recognition method comprises the steps of: generating a support data set based on a reference text of a target domain, wherein the support data set comprises positive example entity pairs belonging to X preset specified relationship types and negative example entity pairs not belonging to the X preset specified relationship types; generating a query data set comprising at least one entity pair in a to-be-tested text of the target domain;and based on the support data set and the query data set, determining a relationship type to which each entity pair in the query data set belongs by using a machine learning model, wherein the machine learning model is pre-trained based on data of other domains except the target domain.

The invention discloses a target detection model training method for visual detection and a target detection method. The training method comprises the following steps of: 1) selecting a plurality of annotated data and non-annotated data during each iterative training; 2) inputting the labeled image sample data into a target detection model for training, and obtaining a prediction result of each labeled image sample data; 3) loss calculation is carried out according to the prediction result and the corresponding label, and the loss Ls of supervision training is obtained; 4) performing weak enhancement and strong enhancement on each piece of unlabeled image sample data; 5) inputting the weakly enhanced sample data into the target detection model for prediction, and taking an obtained prediction result as a pseudo label corresponding to the strongly enhanced sample data; 6) inputting the strongly enhanced sample data into the target detection model for prediction, and performing loss calculation according to an obtained prediction result and a corresponding pseudo label to obtain a loss Lu of unsupervised training; and 7) adjusting parameters of the target detection model according to Ls and Lu.

The invention discloses a financial text classification method and device based on a prompt template and electronic equipment, and the method comprises the steps: obtaining an original financial corpus, and converting the financial corpus into an MLM tag based on the prompt template; converting the MLM tag to generate an MLM tag training sample; constructing a pre-training model, inputting the MLM label training sample into the pre-training model, training the pre-training model, and generating a financial text classification model; and inputting financial text data to be identified into the financial text classification model to obtain a financial text classification result. According to the embodiment of the invention, the classification model can still achieve the generalization ability similar to that of a pre-training model under the condition that only a small amount of labeled data exists; the labeling amount is greatly reduced, and large-scale data dependence is reduced; and the financial text classification efficiency is improved.

The invention provides a keyword generation method, which comprises the following steps of: extracting a first important word in a to-be-mined text based on the to-be-mined text in a first extraction mode; based on a second extraction mode, extracting a second important word in the to-be-mined text; based on the first important word and the second important word, unifying word forms and forming an initial important word set which comprises keywords and non-keywords; based on the initial important word set, non-keywords are removed, and a keyword set is generated. According to the keyword generation method, the important words are extracted in two modes, the annotation amount of the to-be-mined text is reduced, the labor cost is saved, and the keyword extraction accuracy is improved. The embodiment of the invention provides a storage medium comprising the keyword generation method and a keyword generation device. The embodiment of the invention provides a server comprising the keyword set.