Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models. (arXiv:2206.14477v2 [cs.LG] UPDATED)

Training an ensemble of different sub-models has empirically proven to be an
effective strategy to improve deep neural networks' adversarial robustness.
Current ensemble training methods for image recognition usually encode the
image labels by one-hot vectors, which neglect dependency relationships between
the labels. Here we propose a novel adversarial ensemble training approach to
jointly learn the label dependencies and the member models. Our approach
adaptively exploits the learned label dependencies to promote the diversity of
the member models. We test …

adversarial dependencies lg training