Representation Learning for Anomaly Detection: From the Aspects of Data Views and Optimization

University of Technology Sydney. Faculty of Engineering and Information Technology.Anomaly detection is a challenging task in realistic applications. However, there exist major issues for anomaly detection: (1) While the traditional problem setting focuses on data of single view, we have to deal with data of multiple views in many practical scenarios. Current methods often rely on assumptions on data distribution, which limits their flexibility and application. There is a lack of more effective and flexible methods for both semi-supervised and unsupervised multi-view anomaly detection. (2) Deep neural networks (DNN) have been widely applied for detecting anomalies. However, the mainstream optimization and learning strategies in DNN makes it easily fit both normal and anomalous data, resulting in an unsatisfactory performance and less reliability. There is a need for more advanced and reliable optimization framework for anomaly detection. In this thesis, we propose innovative deep representation learning models to tackle anomaly detection problems from aspects of multi-view data and model optimization. We first introduce related work and literature review. In following chapters, we investigate semi-supervised multi-view anomaly detection via variational generative model, unsupervised multi-view anomaly detection by exploring latent spaces, and advanced optimization strategy for general unsupervised anomaly detection respectively

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data

There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Third, emotional data often suffers from missing modalities due to unforeseeable sensor malfunction or configuration issues. In this paper, we address all these problems under a novel multi-view deep generative framework. Specifically, we propose to model the statistical relationships of multi-modality emotional data using multiple modality-specific generative networks with a shared latent space. By imposing a Gaussian mixture assumption on the posterior approximation of the shared latent variables, our framework can learn the joint deep representation from multiple modalities and evaluate the importance of each modality simultaneously. To solve the labeled-data-scarcity problem, we extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. To address the missing-modality problem, we further extend our semi-supervised multi-view model to deal with incomplete data, where a missing view is treated as a latent variable and integrated out during inference. This way, the proposed overall framework can utilize all available (both labeled and unlabeled, as well as both complete and incomplete) data to improve its generalization ability. The experiments conducted on two real multi-modal emotion datasets demonstrated the superiority of our framework.Comment: arXiv admin note: text overlap with arXiv:1704.07548, 2018 ACM Multimedia Conference (MM'18