This paper describes a simple yet effective technique for refining a pretrained classifier network. The proposed AdCorDA method is based on modification of the training set and making use of the duality between network weights and layer inputs. We call this input space training. The method consists of two stages - adversarial correction followed by domain adaptation. Adversarial correction uses adversarial attacks to correct incorrect training-set classifications. The incorrectly classified samples of the training set are removed and replaced with the adversarially corrected samples to form a new training set, and then, in the second stage, domain adaptation is performed back to the original training set. Extensive experimental validations show significant accuracy boosts of over 5% on the CIFAR-100 dataset. The technique can be straightforwardly applied to refinement of weight-quantized neural networks, where experiments show substantial enhancement in performance over the baseline. The adversarial correction technique also results in enhanced robustness to adversarial attacks.

该研究论文描述了一种简单而有效的技术，用于改进预训练的分类器网络。提出的AdCorDA方法基于对训练集的修改，并利用了网络权重和层输入之间的对偶性，称为输入空间训练。该方法由两个阶段组成，即对抗性纠正和领域适应。对抗性纠正使用对抗性攻击来纠正训练集中的错误分类，然后使用纠正后的样本替换原始训练集中的错误样本形成新的训练集，并在第二阶段将域适应应用于原始训练集。广泛的实验证明，在CIFAR-100数据集上显着提高了超过5％的准确性。该技术可以直接应用于权重量化神经网络的改进，实验证明在基准测试上性能得到了显著提升。对抗性纠正技术还增强了对对抗性攻击的鲁棒性。

AdCorDA：通过对抗修正和域适应进行分类器改进