To address the problem of data scarcity, we use an Auxiliary Conditioned Wasserstein Generative Adversarial Network with Gradient Penalty (AC-WGAN-GP) to generate synthetic data. We compare the recognition performance between real and synthetic signals as training data in the task of binary arousal ...
以卷积神经网络作为模型的网络结构避免梯度消失;其次构建合适层数的模型并初始化参数,将训练集输入模型进行训练直至达到迭代次数;最后将训练好的模型应用于滚动轴承故障诊断.该方法改进了原始ACGAN框架,引入Wasserstein距离和梯度惩罚,考虑到滚动轴承振动信号具有周期性和时序性的特点,本发明结合自注意力机制和ACWGANGP来提升...
Figure 1 shows the overall framework for the HSI classification with a small sample size based on the AC-WGAN-GP, which is composed of four parts: the preprocessing based on Gaussian smoothing, the AC-WGAN-GP network, online sample generation, and sample selection algorithm based on KNN. Figu...
基于WGAN‑GP和U‑NET的素描‑照片转化方法属图像处理和异质图像转化领域,本发明首先获取人脸素描‑照片数据库FERET、CUHK、IIIT‑D,进行图片裁剪和调整图片大小,然后对数据进行数据增强,最后用WGAN‑GP和U‑NET生成测试集里素描对应的照片;本发明利用WGAN‑GP解决了梯度爆炸和梯度消失的问题,可较好地生成...
Based on a deep convolutional neural network (DCNN), the model combines a conditional variational autoencoder (DCCVAE) and auxiliary conditional Wasserstein GAN with gradient penalty (ACWGAN-GP) to gradually expand and generate various coating defect images for solving the ...
We train and test the auxiliary classifier WGAN-GP (ACWGAN-GP) model on true and false sticking feature vector samples, developing a breakout prediction method based on computer vision and a generative adversarial network. The test results show that the model can distinguish between true sticking ...