AdCorDA: Classifier Refinement via Adversarial Correction and Domain
  Adaptation
Lulan Shen Ali Edalati Brett Meyer Warren Gross James J. Clark
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.