Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models
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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 our approach on widely used datasets MNIST, FasionMNIST, and CIFAR-10. Results show that our approach is more robust against black-box attacks compared with the state-of-the-art methods. Our code is available at https://github.com/ZJLAB-AMMI/LSD.
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