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Convolutional Neural Networks have become the norm in image classification.Nevertheless, their difficulty to maintain high accuracy across datasets hasbecome apparent in the past few years. In order to utilize such models inreal-world scenarios and applications, they must be able to provide trustworthypredictions on unseen data. In this paper, we argue that extracting featuresfrom a CNN's intermediate layers can assist in the model's final prediction.Specifically, we adapt the Hypercolumns method to a ResNet-18 and find asignificant increase in the model's accuracy, when evaluating on the NICOdataset.

Multi-layer Representation Learning for Robust OOD Image Classification