ConvNets and ImageNet Beyond Accuracy:
Understanding Mistakes and Uncovering Biases
Abstract. ConvNets and ImageNet have driven the recent success of deep learning for image classification. However, the marked slowdown in performance improvement combined with the lack of robustness of neural networks to adversarial
examples and their tendency to exhibit undesirable biases question the reliability
of these methods. This work investigates these questions from the perspective of
the end-user by using human subject studies and explanations. The contribution of
this study is threefold. We first experimentally demonstrate that the accuracy and
robustness of ConvNets measured on Imagenet are vastly underestimated. Next,
we show that explanations can mitigate the impact of misclassified adversarial examples from the perspective of the end-user. We finally introduce a novel tool for
uncovering the undesirable biases learned by a model. These contributions also
show that explanations are a valuable tool both for improving our understanding
of ConvNets’ predictions and for designing more reliable models