  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 margin growth. To counteract this deficiency, we propose to
reward well-classified examples with additive bonuses to revive their
contribution to the learning process. This counterexample theoretically
addresses these three issues. We empirically support this claim by directly
verifying the theoretical results or significant performance improvement with
our counterexample on diverse tasks, including image classification, graph
classification, and machine translation. Furthermore, this paper shows that we
can deal with complex scenarios, such as imbalanced classification, OOD
detection, and applications under adversarial attacks because our idea can
solve these three issues. Code is available at:
https://github.com/lancopku/well-classified-examples-are-underestimated.
