Neural network training method and device and face recognition method and device
A neural network and training method technology, applied in the field of image recognition, can solve the problems of limited face training data acquisition difficulty, increased data storage pressure and computing resource consumption
Pending Publication Date: 2022-08-02
BEIJING SENSETIME TECH DEV CO LTD
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In this way, the data storage pressure and computing resource consumption are greatly increased, and limited by the difficulty of collecting face training data, most of the collected face training data are noisy data
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[0081] In order to make the purposes, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only These are some, but not all, embodiments of the present disclosure. The components of the disclosed embodiments generally described and illustrated herein may be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure is not intended to limit the scope of the disclosure as claimed, but rather to represent only selected embodiments of the disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work fall within the protection sco...
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The invention provides a neural network training method and device and a face recognition method and device.The training method comprises the steps that a first image and at least a preset number of second images are obtained, and the second images and the first image have the same image label; performing feature extraction on the first image by using a target neural network model to obtain first feature information, and performing feature extraction on each second image by using a moving average model of the target neural network model to obtain at least a preset number of second feature information; determining a first loss coefficient based on a first similarity between the first feature information and each piece of second feature information, and determining a second loss coefficient based on a second similarity between the first feature information and third feature information in at least a part of historical feature groups; and determining a target loss based on the first loss coefficient and the second loss coefficient, and performing iterative training on the target neural network model by using the target loss to obtain a trained target neural network model.
Description
technical field [0001] The present disclosure relates to the technical field of image recognition, and in particular, to a neural network training method and device, and a face recognition method and device. Background technique [0002] With the rapid development of computer vision technology, face recognition technology plays an increasingly important role in people's daily life. Most face recognition technologies rely on face recognition neural networks, and the recognition accuracy of face recognition neural networks is closely related to the scale of face training data. [0003] In the process of network training using face training data, it is necessary to store an anchor feature for each category of face labels to determine the network loss. In this way, using a large number of different types of face training data for network training requires storing a large number of anchor features. This greatly increases data storage pressure and computing resource consumption,...
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Login to View More IPC IPC(8): G06V10/774G06V10/82G06V40/16G06V10/764G06V10/74G06V10/40G06V10/30G06N3/04
CPCG06V10/774G06V10/82G06V10/40G06V10/761G06V40/161G06V10/30G06V10/764G06N3/04
Inventor 刘博晓宋广录刘宇
Owner BEIJING SENSETIME TECH DEV CO LTD