  Traditionally, for most machine learning settings, gaining some degree of
explainability that tries to give users more insights into how and why the
network arrives at its predictions, restricts the underlying model and hinders
performance to a certain degree. For example, decision trees are thought of as
being more explainable than deep neural networks but they lack performance on
visual tasks. In this work, we empirically demonstrate that applying methods
and architectures from the explainability literature can, in fact, achieve
state-of-the-art performance for the challenging task of domain generalization
while offering a framework for more insights into the prediction and training
process. For that, we develop a set of novel algorithms including DivCAM, an
approach where the network receives guidance during training via gradient based
class activation maps to focus on a diverse set of discriminative features, as
well as ProDrop and D-Transformers which apply prototypical networks to the
domain generalization task, either with self-challenging or attention
alignment. Since these methods offer competitive performance on top of
explainability, we argue that the proposed methods can be used as a tool to
improve the robustness of deep neural network architectures.
