| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 107 === We propose a semi-supervised convolutional neural network for binary classification, which combines variational autoencoder with generative adversarial network (GAN) to classify similar objects by thresholding the similarities between original images and generated images. Since we only use one-kind samples from the multi-class samples to train the model, this method won’t be affected by the imbalanced data; it means the method is suitable for imbalance data classification.
There are two kinds of improvements in the proposed system, the first one is to improve the training stability of the GAN. It’s well-known that GANs are effective, but training GANs is hard since gradient vanishing, gradient exploding, and mode collapse could be encountered very easily. The second kind of improvements is to make GANs learning better features so that any generated image could look as close as possible to the trained class, even if the input images do not belong to the trained class.
We used X-ray images of electronic components as examples in our experiment. We got nearly 94% true positive rate for every classes by using a simple similarity criterion.
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