  Convolutional Neural Networks have become the norm in image classification.
Nevertheless, their difficulty to maintain high accuracy across datasets has
become apparent in the past few years. In order to utilize such models in
real-world scenarios and applications, they must be able to provide trustworthy
predictions on unseen data. In this paper, we argue that extracting features
from 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 a
significant increase in the model's accuracy, when evaluating on the NICO
dataset.
