Deep Clustering: A Comprehensive Survey

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering

Self-supervised learning of a facial attribute embedding from video

We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time. To perform this task, we introduce a network, Facial Attributes-Net (FAb-Net), that is trained to embed multiple frames from the same video face-track into a common low-dimensional space. With this approach, we make three contributions: first, we show that the network can leverage information from multiple source frames by predicting confidence/attention masks for each frame; second, we demonstrate that using a curriculum learning regime improves the learned embedding; finally, we demonstrate that the network learns a meaningful face embedding that encodes information about head pose, facial landmarks and facial expression, i.e. facial attributes, without having been supervised with any labelled data. We are comparable or superior to state-of-the-art self-supervised methods on these tasks and approach the performance of supervised methods.Comment: To appear in BMVC 2018. Supplementary material can be found at http://www.robots.ox.ac.uk/~vgg/research/unsup_learn_watch_faces/fabnet.htm

A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

Airspace complexity is a critical metric in current Air Traffic Management systems for indicating the security degree of airspace operations. Airspace complexity can be affected by many coupling factors in a complicated and nonlinear way, making it extremely difficult to be evaluated. In recent years, machine learning has been proved as a promising approach and achieved significant results in evaluating airspace complexity. However, existing machine learning based approaches require a large number of airspace operational data labeled by experts. Due to the high cost in labeling the operational data and the dynamical nature of the airspace operating environment, such data are often limited and may not be suitable for the changing airspace situation. In light of these, we propose a novel unsupervised learning approach for airspace complexity evaluation based on a deep neural network trained by unlabeled samples. We introduce a new loss function to better address the characteristics pertaining to airspace complexity data, including dimension coupling, category imbalance, and overlapped boundaries. Due to these characteristics, the generalization ability of existing unsupervised models is adversely impacted. The proposed approach is validated through extensive experiments based on the real-world data of six sectors in Southwestern China airspace. Experimental results show that our deep unsupervised model outperforms the state-of-the-art methods in terms of airspace complexity evaluation accuracy