Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models. (arXiv:2206.14477v1 [cs.LG])

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 training approach that learns
the conditional dependencies between labels and the model ensemble jointly. We
test our approach on widely used datasets MNIST, FasionMNIST and CIFAR-10.
Results show that our approach …

adversarial dependencies lg training