The powerful feature representation and nonlinear fitting capability of deep neural networks stem from sufficient learning on high-quality datasets. However, in practical engineering applications, due to economic and labor costs, acquiring a large amount of typical labeled data becomes extremely challenging, resulting in avery limited number of training samples. Data augmentation methods provide a simple yet effective approach to address such issues. The data augmentation method based on the deep Generative Adversarial Network (GAN) model has shown exceptional performance in learning complex high-dimensional data distributions, offering a new perspective on solving data problems.
The GAN network, short for Generative Adversarial Network, is a type of deep learning model initially proposed by Ian Goodfellow et al. in 2014. The GAN network mainly consists of two parts: the generator and the discriminator. The generator’s task is to receive a random noise vector as input and generate samples that closely resemble real data, while the discriminator acts as a binary classifier aimed at distinguishing whether the input samples are generated by the generator or come from real data.
During the training process, the generator and discriminator engage in adversarial learning, where the generator’s goal is to produce samples that can deceive the discriminator, while the discriminator’s goal is to accurately assess the authenticity of the samples. This adversarial process continues until Nash equilibrium is reached, at which point the generator’s capability is strong enough that the discriminator cannot effectively distinguish between real and fake samples.
The GAN network has demonstrated powerful capabilities across various fields, including but not limited to image generation, image style transfer, super-resolution, data augmentation, video generation, natural language processing, medical image processing, and gaming and virtual reality.
This issue shares the Matlab code for the data generation method based on the 1D-GAN, effectively addressing the problem of insufficient data.
This session utilizes a classic dataset from machine learning, the iris dataset: this dataset has 3 classes, each with 50 samples, with each sample having 4 features, totaling 150 samples * 4 features.Using GAN to generate some data and selecting SVM as the classifier (other classifiers can also be used) for classification, to simply verify the quality of the generated GAN data. The generated data is used as the training set to train the SVM classifier, while the original data is used as the test set for evaluation.The final results include the distribution of the original data and the generated data, the probability density function distribution, and boxplots for each feature.
The final SVM classification accuracy: training accuracy 96.5333, test accuracy 96.667.
Results Visualization:
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