Well-classified Examples are Underestimated in Classification with Deep Neural Networks

The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary. For instance, when training with cross-entropy loss, examples with higher likelihoods (i.e., well-classified examples) contribute smaller gradients in back-propagation. However, we theoretically show that this common practice hinders representation learning, energy optimization, and the growth of margin. To counteract this deficiency, we propose to reward well-classified examples with additive bonuses to revive their contribution to learning. This counterexample theoretically addresses these three issues. We empirically support this claim by directly verify the theoretical results or through the significant performance improvement with our counterexample on diverse tasks, including image classification, graph classification, and machine translation. Furthermore, this paper shows that because our idea can solve these three issues, we can deal with complex scenarios, such as imbalanced classification, OOD detection, and applications under adversarial attacks.