Open Set Deep Learning with A Bayesian Nonparametric Generative Model
Xulun Ye, Jieyu Zhao · 2019
Being a widely studied model in machine learning and multimedia community, Deep Neural Network (DNN) has achieved an encouraging success in various applications. However, conventional DNN suffers the difficulty when handling the open set learning problem, in which the true class number is unknown, and the predication label in the testing dataset usually has unseen classes which are not contained in the training set. In this paper, we aim to tackle this problem by unifying deep neural network and Dirichlet process mixture model. Firstly, to learn the deep feature and enable the incorporation of DNN and the Bayesian nonparametric model, we extend deep metric learning to a semi-supervised framework. Secondly, with the learned deep feature, we construct our open set classification method by expanding the Dirichlet process mixture model to a semi-supervised framework. To infer our semi-supervised Bayesian model, the corresponding variational inference algorithm has also been derived. Experiment on synthetic and real world datasets validates our theory analysis and demonstrates the state-of-the-art performance.