OPML: A One-Pass Closed-Form Solution for Online Metric Learning
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Computing and Information Technology, University of Wollongong（伍伦贡大学计算与信息技术学院）
Wenbin Li, Yang Gao, Lei Wang, Luping Zhou, Jing Huo, Yinghuan Shi
To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet construction strategy, which aims to use only a very small number of triplets to approximate the representation ability of whole original triplets obtained by batch-manner methods. Then, OPML employs a closed-form solution to update the metric for new coming samples, which leads to a low space (i.e., $O(d)$) and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality. In addition, an extension of OPML (namely COPML) is further proposed to enhance the robustness when in real case the first several samples come from the same class (i.e., cold start problem). In the experiments, we have systematically evaluated our methods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detection in videos, which aims to fully evaluate the proposed methods on different sample number, different feature dimensionalities and different feature extraction ways (i.e., hand-crafted and deeply-learned). The results show that OPML and COPML can obtain the promising performance with a very low computational cost. Also, the effectiveness of COPML under the cold start setting is experimentally verified.
Adaptive Subgradient Methods for Online AUC Maximization
The University of Chicago（芝加哥大学）；Singapore Management University（新加坡管理大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Nanyang Technological University（南洋理工大学）
Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update
RIKEN AIP（日本理化学研究所高级研究所）；National Key Laboratory for Novel Software Technology（国家新型软件技术实验室）；School of Artificial Intelligence（人工智能学院）；The University of Tokyo（东京大学）
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation
South China University of Technology（华南理工大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Southern Medical University（南方医科大学）；UBTech Sydney Artificial Intelligence Centre（优必选悉尼人工智能中心）；The University of Sydney（悉尼大学）
