  Deep neural networks have made breakthroughs in a wide range of visual
understanding tasks. A typical challenge that hinders their real-world
applications is that unknown samples may be fed into the system during the
testing phase, but traditional deep neural networks will wrongly recognize
these unknown samples as one of the known classes. Open set recognition (OSR)
is a potential solution to overcome this problem, where the open set classifier
should have the flexibility to reject unknown samples and meanwhile maintain
high classification accuracy in known classes. Probabilistic generative models,
such as Variational Autoencoders (VAE) and Adversarial Autoencoders (AAE), are
popular methods to detect unknowns, but they cannot provide discriminative
representations for known classification. In this paper, we propose a novel
framework, called Conditional Probabilistic Generative Models (CPGM), for open
set recognition. The core insight of our work is to add discriminative
information into the probabilistic generative models, such that the proposed
models can not only detect unknown samples but also classify known classes by
forcing different latent features to approximate conditional Gaussian
distributions. We discuss many model variants and provide comprehensive
experiments to study their characteristics. Experiment results on multiple
benchmark datasets reveal that the proposed method significantly outperforms
the baselines and achieves new state-of-the-art performance.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning