We show that utilizing attribution maps for training neural networks can
improve regularization of models and thus increase performance. Regularization
is key in deep learning, especially when training complex models on relatively
small datasets. In order to understand inner workings of neural networks,
attribution methods such as Layer-wise Relevance Propagation (LRP) have been
extensively studied, particularly for interpreting the relevance of input
features. We introduce Challenger, a module that leverages the explainable
power of attribution maps in order to manipulate particularly relevant input
patterns. Therefore, exposing and subsequently resolving regions of ambiguity
towards separating classes on the ground-truth data manifold, an issue that
arises particularly when training models on rather small datasets. Our
Challenger module increases model performance through building more diverse
filters within the network and can be applied to any input data domain. We
demonstrate that our approach results in substantially better classification as
well as calibration performance on datasets with only a few samples up to
datasets with thousands of samples. In particular, we show that our generic
domain-independent approach yields state-of-the-art results in vision, natural
language processing and on time series tasks.

本文研究表明利用归因映射来训练神经网络能够提高模型的正则化从而提高性能，并介绍了一种名为 Challenger 的模块，该模块利用输入模式的解释能力来操纵重要的输入模式，从而提高模型性能。