Explore how NEO-KD outperforms SKD and ARD in multi-exit networks and discover advanced adversarial attack methods tailored for enhanced resilience.

Explore how NEO-KD outperforms the adapted TEAT defense methods for single-exit networks in multi-exit scenarios.

Explore the reasons behind the performance decline of later exits in NEO-KD networks during adversarial testing and discover strategies to enhance accuracy.

Explore the impact of hyperparameter tuning on NEO-KD's performance, focusing on the optimal values of α, β, and γ that enhance adversarial test accuracy.

Explore NEO-KD's clean test accuracy compared to baseline models and its performance in adversarial training with max-average and average attacks on CIFAR-100.

Explore the detailed experimentation methods for NEO-KD, including model training, adversarial strategies, and how to determine confidence thresholds.

NEO-KD enhances adversarial robustness in multi-exit networks by reducing transferability and improving test accuracy.

NEO-KD achieves state-of-the-art adversarial accuracy by combining NKD and EOKD, optimizing ensemble strategies, and showing resilience to stronger adversarial

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.

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.