  The generalization capability of deep neural networks has been substantially
improved by applying a wide spectrum of regularization methods, e.g.,
restricting function space, injecting randomness during training, augmenting
data, etc. In this work, we propose a simple yet effective regularization
method named progressive self-knowledge distillation (PS-KD), which
progressively distills a model's own knowledge to soften hard targets (i.e.,
one-hot vectors) during training. Hence, it can be interpreted within a
framework of knowledge distillation as a student becomes a teacher itself.
Specifically, targets are adjusted adaptively by combining the ground-truth and
past predictions from the model itself. We show that PS-KD provides an effect
of hard example mining by rescaling gradients according to difficulty in
classifying examples. The proposed method is applicable to any supervised
learning tasks with hard targets and can be easily combined with existing
regularization methods to further enhance the generalization performance.
Furthermore, it is confirmed that PS-KD achieves not only better accuracy, but
also provides high quality of confidence estimates in terms of calibration as
well as ordinal ranking. Extensive experimental results on three different
tasks, image classification, object detection, and machine translation,
demonstrate that our method consistently improves the performance of the
state-of-the-art baselines. The code is available at
https://github.com/lgcnsai/PS-KD-Pytorch.
