Continuous learning image classification method and device based on deep learning

A technology of deep learning and classification methods, applied in the field of continuous learning image classification based on deep learning

Active Publication Date: 2022-05-10
SUN YAT SEN UNIV
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But this extension-based approach inevitably changes the structure of the classifier, especially the feature extractor part, and requires more and more

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  • Continuous learning image classification method and device based on deep learning
  • Continuous learning image classification method and device based on deep learning
  • Continuous learning image classification method and device based on deep learning

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[0042] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution of the present invention will be described clearly and completely below with reference to the embodiments of the present application and the accompanying drawings. It should be understood that the accompanying drawings are only for exemplary purposes. The description should not be construed as a limitation on this patent. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of this application.

[0043] Reference in this application to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least o...

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Abstract

The invention discloses a continuous learning image classification method and device based on deep learning, and the method comprises the following steps: constructing a task continuous learning model with task specific batch normalization, and enabling parameters of all convolution kernels in a feature extractor of the task continuous learning model to be fixed in all tasks, when each new task is learned, parameters of a corresponding batch normalization layer BN in each convolution kernel and a specific classification head of the task are learned together; incremental training is carried out on the task continuous learning model, and when a new task comes, a specific batch normalization layer and a classification head of the new task are added; after incremental training is completed, a trained task continuous learning model is obtained, image tasks to be classified are input into the trained task continuous learning model, and the classification tasks are completed. According to the method, the problem of disastrous forgetting is effectively solved by utilizing batch normalized BN existing in a task continuous learning model.

Description

technical field [0001] The present invention relates to the technical field of image classification, in particular to a continuous learning image classification method and device based on deep learning. Background technique [0002] Artificial intelligence, especially deep learning, has achieved great success in image classification. However, there is a problem with the model, the catastrophic forgetting problem. That is: after learning new knowledge, the previous training content is almost completely forgotten. In short, such a problem is that in the process of continuous learning, in the learning of each task, the model will be updated with different data, and learning new tasks will greatly reduce the performance of old tasks. Therefore, it is crucial that deep learning models have a continuous learning ability: that is, the ability to continuously learn new tasks without forgetting how to perform previously trained tasks. [0003] In recent years, a variety of methods...

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Application Information

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IPC IPC(8): G06V10/82G06V10/44G06V10/764G06V10/774G06N3/04G06N3/08G06K9/62
CPCG06N3/08G06N3/047G06N3/048G06N3/045G06F18/2415G06F18/214
Inventor 许俊杰王瑞轩谢旭辰黄钰竣
Owner SUN YAT SEN UNIV
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