Machine learning with adversarial perturbations and noisy labels

Machine learning models such as traditional random forests (RFs) and modern deep neural networks (DNNs) have been successfully used to solve complex learning problems in many applications such as speech recognition, image classification, face recognition, gaming agents and self-driving cars.For example, DNNs have demonstrated near or even surpassing human-level performance in image classification tasks.Despite their current success, these models are still vulnerable to noisy real-world situations where illegitimate or noisy data may exist to corrupt learning.Studies have shown that by adding small, human imperceptible (in the case of images) adversarial perturbations, normal samples can be perturbed into "adversarial examples", and DNNs can be made to misclassify adversarial examples with a high level of confidence.This arouses security concerns when employing DNNs in security-sensitive applications such as fingerprint recognition, face verification and autonomous cars.Studies have also found that DNNs can overfit to noisy (incorrect) labels and as a result, generalize poorly.This has been one of the key challenges when applying DNNs in noisy real-world scenarios where even high-quality datasets tend to contain noisy labels.Another open question in machine learning is whether actionable knowledge (or feedback) can be generated from prediction models to support decision making towards some long-term learning goals (for example, mastering a certain type of skills in a simulation-based learning (SBL) environment).We view the feedback generation problem from a new perspective of adversarial perturbation, and explore the possibility of using adversarial techniques to generate feedback.In this thesis, we investigate machine learning models including DNNs and RFs, and their learning behavior through the lens of adversarial perturbations iii Declaration This is to certify that 1. the thesis comprises only my original work towards the degree of Doctor of Philosophy except where indicated in the Preface, 2. due acknowledgment has been made in the text to all other material used, 3. the thesis is fewer than 80,000 words in length, exclusive of tables, maps, bibliographies and appendices.