FAR: A General Framework for Attributional Robustness
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Attribution maps have gained popularity as tools for explaining neural networks predictions. By assigning an importance value to each input dimension that represents their influence towards the outcome, they give an intuitive explanation of the decision process. However, recent work has discovered vulnerability of these maps to imperceptible, carefully crafted changes in the input that lead to significantly different attributions, rendering them meaningless. By borrowing notions of traditional adversarial training - a method to achieve robust predictions - we propose a novel framework for attributional robustness (FAR) to mitigate this vulnerability. Central assumption is that similar inputs should yield similar attribution maps, while keeping the prediction of the network constant. Specifically, we define a new generic regularization term and training objective that minimizes the maximal dissimilarity of attribution maps in a local neighbourhood of the input. We then show how current state-of-the-art methods can be recovered through principled instantiations of these objectives. Moreover, we propose two new training methods, AAT and AdvAAT, derived from the framework, that directly optimize for robust attributions and predictions. We showcase the effectivity of our training methods by comparing them to current state-of-the-art attributional robustness approaches on widely used vision datasets. Experiments show that they perform better or comparably to current methods in terms of attributional robustness, while being applicable to any attribution method and input data domain. We finally show that our methods mitigate undesired dependencies of attributional robustness and some training and estimation parameters, which seem to critically affect other methods.
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