Tuning a pre-trained network is commonly thought to improve data efficiency. However, Kaiming He et al. have called into question the utility of pre-training by showing that training from scratch can often yield similar performance, should the model train long enough. We show that although pre-training may not improve performance on traditional classification metrics, it does provide large benefits to model robustness and uncertainty. Through extensive experiments on label corruption, class imbalance, adversarial examples, out-of-distribution detection, and confidence calibration, we demonstrate large gains from pre-training and complementary effects with task-specific methods. We show approximately a 30% relative improvement in label noise robustness and a 10% absolute improvement in adversarial robustness on CIFAR-10 and CIFAR-100. In some cases, using pre-training without task-specific methods surpasses the state-of-the-art, highlighting the importance of using pre-training when evaluating future methods on robustness and uncertainty tasks.

本文研究了深度学习中预训练对模型鲁棒性和不确定性预测的影响，并通过对对抗性环境、标签结构、类分布、数据分布等进行大量实验，展示了其中很大的性能提升。同时，提出了对抗性预训练方法，实现了不错的性能提升。

使用预训练可提高模型的鲁棒性和不确定性