AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation
CoRR(2024)
摘要
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
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要