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VSA-CGAN: An Intelligent Generation Model for Deep Learning Sample Database Construction
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DOI：10.1109/ACCESS.2020.3012185.png)
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Abstract
En 中文
In order to solve the problem of model accuracy reduction caused by the difficulty of obtaining specific training samples or the insufficient number of samples in the application of existing object detection and recognition model based on deep learning, this article proposes a conditional generative adversarial network model (VSA-CGAN), which integrates the self-attention mechanism of visual perception to optimize the inference of object attention feature maps, so as to learn the global information of the image and the detailed features of the object. It is designed to add conditional features in the generator and the discriminator, associate the specific dimensions of the data with the semantic features, and explicitly indicate the model to generate the corresponding object signature category information, so as to generate the feature representation of the image which is more suitable for the distribution of the original data. The model in this article has completed numerical experiments on several general standard data sets, and compared with several mainstream generative adversarial network models in image data augmentation performance. The experimental results show that the generation model in this article has excellent object simulation ability and strong application prospects.
Keywords:
Generative adversarial networks
Data models
Generators
Gallium nitride
Feature extraction
Numerical models
Machine learning
Generative adversarial network
attention mechanism
visual salience
object simulation
deep learning
data augmentation
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