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

The invention provides a neural network model training method and device, electronic equipment and a computer readable storage medium. The neural network model training method of the neural network model comprises the following steps: executing initial training by utilizing a first training sample set to obtain an initial neural network model; performing prediction on the second training sample set by utilizing the initial neural network model to obtain a prediction result of each training sample in the second training sample set; determining a plurality of preferred samples from the second training sample set based on the prediction result; receiving a labeling result for the plurality of preferred samples, and adding the labeled plurality of preferred samples into a first training sample set to obtain an expanded first training sample set; performing update training by using the extended first training sample set to obtain an updated neural network model; under the condition that a training ending condition is met, ending the training; and repeating the prediction step, the preferred sample determination step, the sample expansion step and the training updating step under the condition that the training ending condition is not met.

The invention proposes a speech recognition method based on domain invariant features, and applies the speech domain invariant feature extraction model to the end-to-end speech recognition model. The feature extraction model used in the present invention aims at the problem of robustness. By adding more types of voice data to train the voice feature extraction model, better parameters can be obtained, and a better domain-invariant feature extraction model can be obtained. The speech recognition method based on domain-invariant features, using unlabeled pure speech data to train the feature extraction model, and using a small amount of speech with text annotations to train the end-to-end acoustic model, provides an important way to improve the robustness of the end-to-end acoustic model. technical support. Compared with the prior art, the present invention has higher recognition accuracy in different noise environments, a smaller task load of voice tagging tasks, and faster model training and testing speeds.

The invention relates to a breast electronic medical record entity recognition system based on multi-standard active learning, which is characterized in that it comprises: a preprocessing module; an entity recognition module; and an active learning module. The present invention considers three aspects of labeling data volume, sentence labeling cost, and data sampling balance, and designs an active learning selection strategy for text sequence labeling to reduce the total labeling workload. On the one hand, the present invention can be used to build systems such as identification marks for breast disease risk patients, disease drug recommendation, decision-making assistance, etc., to help doctors improve the implementation efficiency of standardized diagnosis and treatment of breast diseases, and to provide scientific basis and suggestions; on the other hand, it can also assist Doctors discover potential abnormalities in the diagnosis and treatment process, reduce misdiagnosis and missed diagnosis rates, and improve the cure rate of patients with breast diseases, which is of great value to the intelligent development of breast disease research.

The invention provides a semi-automatic labeling method for image data, which includes: performing partial image-level labeling on unlabeled images; putting the labeled data into a collaborative weakly supervised recognition model for training; transforming the collaborative weakly supervised recognition model to obtain strong Supervised transformation model; use the strong supervision transformation model to detect unlabeled images and obtain the detection results; use artificially labeled image data to train the strong supervision transformation model. The method of the present invention uses a certain amount of weakly labeled data in the early stage, and then uses active learning to gradually improve the model in the later stage, thereby ensuring accuracy while reducing the amount of labeling.