  Out-of-distribution (OOD) detection aims to discern outliers from the
intended data distribution, which is crucial to maintaining high reliability
and a good user experience. Most recent studies in OOD detection utilize the
information from a single representation that resides in the penultimate layer
to determine whether the input is anomalous or not. Although such a method is
straightforward, the potential of diverse information in the intermediate
layers is overlooked. In this paper, we propose a novel framework based on
contrastive learning that encourages intermediate features to learn
layer-specialized representations and assembles them implicitly into a single
representation to absorb rich information in the pre-trained language model.
Extensive experiments in various intent classification and OOD datasets
demonstrate that our approach is significantly more effective than other works.
