  The field of deep learning is evolving in different directions, with still
the need for more efficient training strategies. In this work, we present a
novel and robust training scheme that integrates visual explanation techniques
in the learning process. Unlike the attention mechanisms that focus on the
relevant parts of images, we aim to improve the robustness of the model by
making it pay attention to other regions as well. Broadly speaking, the idea is
to distract the classifier in the learning process to force it to focus not
only on relevant regions but also on those that, a priori, are not so
informative for the discrimination of the class. We tested the proposed
approach by embedding it into the learning process of a convolutional neural
network for the analysis and classification of two well-known datasets, namely
Stanford cars and FGVC-Aircraft. Furthermore, we evaluated our model on a
real-case scenario for the classification of egocentric images, allowing us to
obtain relevant information about peoples' lifestyles. In particular, we work
on the challenging EgoFoodPlaces dataset, achieving state-of-the-art results
with a lower level of complexity. The obtained results indicate the suitability
of our proposed training scheme for image classification, improving the
robustness of the final model.
