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.

翻译：通过应用广泛的正规化方法,例如限制功能空间、在培训期间注射随机性、增加数据等等,深神经网络的总体化能力已大为改善。在这项工作中,我们提出一个简单而有效的正规化方法,名为渐进自学蒸馏(PS-KD),逐步提炼模型本身的知识,以在培训期间软化硬目标(即一热矢量),因此,可以在知识蒸馏框架内解释,因为学生本身成为教师,具体地说,通过将模型本身的地盘和过去的预测结合起来,对目标进行了适应性调整。我们表明,PS-KD是一种硬性实例开采的效果,根据对实例的分类困难程度调整梯度。拟议方法适用于任何具有硬目标的监督性学习任务,并且很容易与现有的正规化方法相结合,以进一步提高总体化绩效。此外,PS-KD不仅实现了更高的准确性,而且还通过在校准目标方面提供高质量的信心估算,以及从现有的或硬质的图像的转换,我们现有的机器的排序,即不断的测试和不断改进的模型。