  Deep neural networks have achieved state-of-the-art performance in a wide
range of recognition/classification tasks. However, when applying deep learning
to real-world applications, there are still multiple challenges. A typical
challenge is that unknown samples may be fed into the system during the testing
phase and traditional deep neural networks will wrongly recognize the unknown
sample as one of the known classes. Open set recognition is a potential
solution to overcome this problem, where the open set classifier should have
the ability to reject unknown samples as well as maintain high classification
accuracy on known classes. The variational auto-encoder (VAE) is a popular
model to detect unknowns, but it cannot provide discriminative representations
for known classification. In this paper, we propose a novel method, Conditional
Gaussian Distribution Learning (CGDL), for open set recognition. In addition to
detecting unknown samples, this method can also classify known samples by
forcing different latent features to approximate different Gaussian models.
Meanwhile, to avoid information hidden in the input vanishing in the middle
layers, we also adopt the probabilistic ladder architecture to extract
high-level abstract features. Experiments on several standard image datasets
reveal that the proposed method significantly outperforms the baseline method
and achieves new state-of-the-art results.
