  The objective of Open set recognition (OSR) is to learn a classifier that can
reject the unknown samples while classifying the known classes accurately. In
this paper, we propose a self-supervision method, Detransformation Autoencoder
(DTAE), for the OSR problem. This proposed method engages in learning
representations that are invariant to the transformations of the input data.
Experiments on several standard image datasets indicate that the pre-training
process significantly improves the model performance in the OSR tasks.
Meanwhile, our proposed self-supervision method achieves significant gains in
detecting the unknown class and classifying the known classes. Moreover, our
analysis indicates that DTAE can yield representations that contain more target
class information and less transformation information than RotNet.
