  Image classification methods are usually trained to perform predictions
taking into account a predefined group of known classes. Real-world problems,
however, may not allow for a full knowledge of the input and label spaces,
making failures in recognition a hazard to deep visual learning. Open set
recognition methods are characterized by the ability to correctly identify
inputs of known and unknown classes. In this context, we propose GeMOS: simple
and plug-and-play open set recognition modules that can be attached to
pretrained Deep Neural Networks for visual recognition. The GeMOS framework
pairs pre-trained Convolutional Neural Networks with generative models for open
set recognition to extract open set scores for each sample, allowing for
failure recognition in object recognition tasks. We conduct a thorough
evaluation of the proposed method in comparison with state-of-the-art open set
algorithms, finding that GeMOS either outperforms or is statistically
indistinguishable from more complex and costly models.
