  Convolutional Neural Networks (CNNs) have become the state-of-the-art method
to learn from image data. However, recent research shows that they may include
a texture and colour bias in their representation, contrary to the intuition
that they learn the shapes of the image content and to human biological
learning. Thus, recent works have attempted to increase the shape bias in CNNs
in order to train more robust and accurate networks on tasks. One such approach
uses style-transfer in order to remove texture clues from the data. This work
reproduces this methodology on four image classification datasets, as well as
extends the method to use domain-adversarial training in order to further
increase the shape bias in the learned representation. The results show the
proposed method increases the robustness and shape bias of the CNNs, while it
does not provide a gain in accuracy.
