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Understanding Adversarial Training: Improve Image Recognition Accuracy Of Convolution Neural Network, Naoki Ishibashi

Dissertations and Theses

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits. Recently many researchers work on Convolution Neural Network for image recognition, and get results as good as human being. Additionally, Image recognition task is getting more popular and high demand to apply to other fields, but also there are still many problems to utilize in everyday life. One of these problems is that several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that …