| dc.description.abstract | Whether it is detection or identification in deep learning applications, the biggest factor affecting prediction accuracy is amount of data, especially training data. Therefore, collecting data is the most important issue in deep learning. As far as the problem of binary classification, data need to achieve balance. If there are a lot of data from correct samples and few data from defective samples, the results of prediction or testing are likely to be wrong due to imbalance of data. In our research, we will use the generative adversarial network to generate properly defective samples to solve the problem of data imbalance.
our research is divided into two steps. The first step is to generate reasonable defective samples, and the second step is to verify and compare the synthetic defective samples.
The results of the generative adversarial network are good, but training is difficult. In addition to the possibility of encountering gradient disappearance or gradient explosion, it is also easy to encounter the problem of model collapse which is the lack of diversity in the generated images. We used different training methods, expected to lead the network more stable during training.
In addition to training stability, we also expect that the generated samples can have particularly defective features. we create four different network architectures from different perspectives to learn the defective features and synthesize defective images. First, we use adaptive instance normalization to learn the features of each resolution layer. Because features expressed by each different resolution are not the same, we observe the meaning of each resolution expression, then make the generated samples more meaningful. Second, we use modulation and demodulation methods to learn the characteristics of each resolution layer, and improve the quality of the generated samples through skip connection and residual network. Third, we use adversarial variational autoencoder to learn the defect features from the encoder and decoder. After training, we input the normal samples, so that the normal samples are fused with the defect features to get the defective images which we want. Finally, replace the adversarial variational autoencoder architecture to improve the generative effect.
In our experiment, we used styleGAN to mix style, and found the best match among the random number combination of normal samples and defective samples. In addition, we used adversarial variational autoencoder to replace different architectures. Since then, the SSIM value has improved significantly. | en_US |