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Neural network training method and device and image processing method and device

A neural network training and neural network technology, applied in the fields of neural network training methods and devices and image processing methods and devices, can solve the problems of complex training process, difficulty in training and application of noisy data sets, etc., so as to simplify the training process and simplify the network. effect of structure

Active Publication Date: 2019-09-06
BEIJING SENSETIME TECH DEV CO LTD
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Problems solved by technology

[0003] In related technologies, it is usually necessary to pre-assume the noise distribution of the label, add additional supervision data, or design an auxiliary network to achieve network training on noise datasets without accurate labels. The training and application on the

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  • Neural network training method and device and image processing method and device
  • Neural network training method and device and image processing method and device
  • Neural network training method and device and image processing method and device

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Embodiment Construction

[0033] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numbers in the figures indicate functionally identical or similar elements. While various aspects of the embodiments are shown in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0034] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as superior or better than other embodiments.

[0035] The term "and / or" in this article is just an association relationship describing associated objects, which means that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and there exists alone B these three situations. In addition, the term "at least one" herein mean...

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Abstract

The invention relates to a neural network training method and device and an image processing method and device. The training method comprises the following steps: carrying out classification processing on a target image in a training set through a neural network to obtain a prediction classification result of the target image; and training the neural network according to the prediction classification result, the initial category label and the correction category label of the target image. According to the embodiment of the invention, the training process of the neural network can be supervisedby initializing and correcting the category label, and the training process and the network structure are simplified.

Description

technical field [0001] The present disclosure relates to the field of computer technology, in particular to a neural network training method and device, and an image processing method and device. Background technique [0002] With the continuous development of artificial intelligence technology, machine learning (especially deep learning) has achieved good results in many fields such as computer vision. The current machine learning (deep learning) has a strong dependence on large-scale accurately labeled data sets, but it is very time-consuming and expensive to collect large-scale accurately labeled data sets. A new branch of machine learning seeks to train networks on imprecisely labeled 'noisy data' to improve network generalization and reduce the cost of collecting the required data. [0003] In related technologies, it is usually necessary to pre-assume the noise distribution of the label, add additional supervision data, or design an auxiliary network to achieve networ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06V10/762G06V10/764
CPCG06N3/08G06N3/045G06F18/23G06F18/241G06V10/82G06V10/761G06V10/762G06V10/7715G06V10/764G06F18/22G06F18/24137
Inventor 韩江帆罗平王晓刚
Owner BEIJING SENSETIME TECH DEV CO LTD
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