  Deep Neural Networks are powerful models that attained remarkable results on
a variety of tasks. These models are shown to be extremely efficient when
training and test data are drawn from the same distribution. However, it is not
clear how a network will act when it is fed with an out-of-distribution
example. In this work, we consider the problem of out-of-distribution detection
in neural networks. We propose to use multiple semantic dense representations
instead of sparse representation as the target label. Specifically, we propose
to use several word representations obtained from different corpora or
architectures as target labels. We evaluated the proposed model on computer
vision, and speech commands detection tasks and compared it to previous
methods. Results suggest that our method compares favorably with previous work.
Besides, we present the efficiency of our approach for detecting wrongly
classified and adversarial examples.
