NEO-KD outperforms baselines in both anytime and budgeted prediction setups, saving computing power while improving adversarial test accuracy.

Evaluate NEO-KD's performance on five datasets including CIFAR and ImageNet, comparing it against various baselines in both adversarial robustness and inference

Discover NEO-KD, a distillation strategy combining NKD and EOKD, to enhance adversarial robustness and reduce transferability in multi-exit neural networks.

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Explore adversarial training for multi-exit networks using single, max-average, and average attacks, targeting submodels to reduce adversarial transferability.

Explore the role of knowledge distillation in improving adversarial robustness for multi-exit networks.

Discover NEO-KD, a knowledge-distillation strategy that boosts adversarial robustness in multi-exit neural networks.

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