Due to numerous breakthroughs in real-world applications brought by machine
intelligence, deep neural networks (DNNs) are widely employed in critical
applications. However, predictions of DNNs are easily manipulated with
imperceptible adversarial perturbations, which impedes the further deployment
of DNNs and may result in profound security and privacy implications. By
incorporating adversarial samples into the training data pool, adversarial
training is the strongest principled strategy against various adversarial
attacks among all sorts of defense methods. Recent works mainly focus on
developing new loss functions or regularizers, attempting to find the unique
optimal point in the weight space. But none of them taps the potentials of
classifiers obtained from standard adversarial training, especially states on
the searching trajectory of training. In this work, we are dedicated to the
weight states of models through the training process and devise a simple but
powerful \emph{Self-Ensemble Adversarial Training} (SEAT) method for yielding a
robust classifier by averaging weights of history models. This considerably
improves the robustness of the target model against several well known
adversarial attacks, even merely utilizing the naive cross-entropy loss to
supervise. We also discuss the relationship between the ensemble of predictions
from different adversarially trained models and the prediction of
weight-ensembled models, as well as provide theoretical and empirical evidence
that the proposed self-ensemble method provides a smoother loss landscape and
better robustness than both individual models and the ensemble of predictions
from different classifiers. We further analyze a subtle but fatal issue in the
general settings for the self-ensemble model, which causes the deterioration of
the weight-ensembled method in the late phases.

本文提出了一种简单而强大的自我整合对抗训练方法 (SEAT)，通过在训练数据池中加入对抗样本来提高目标模型的鲁棒性，并通过历史模型的权重平均值产生一个健壮的分类器，该方法比起其他防御策略在各种对抗攻击下提高了很多的鲁棒性

自组合对抗训练提高鲁棒性

Self-Ensemble Adversarial Training for Improved Robustness

Fine-tuning pre-trained language models like BERT has become an effective way
in NLP and yields state-of-the-art results on many downstream tasks. Recent
studies on adapting BERT to new tasks mainly focus on modifying the model
structure, re-designing the pre-train tasks, and leveraging external data and
knowledge. The fine-tuning strategy itself has yet to be fully explored. In
this paper, we improve the fine-tuning of BERT with two effective mechanisms:
self-ensemble and self-distillation. The experiments on text classification and
natural language inference tasks show our proposed methods can significantly
improve the adaption of BERT without any external data or knowledge.

本文提出了两种有效机制，即自我集成和自我蒸馏来改进 BERT 的微调，实验证明这些方法可以显着提高 BERT 的适应性。