#multi-exit-networks

#multi-exit-networks

NEO-KD significantly enhances the performance of multi-exit neural networks, especially during adversarial training, compared to traditional self-distillation techniques.

NEO-KD algorithm is superior for adversarial defense in multi-exit networks compared to adapted single-exit strategies.

NEO-KD enhances adversarial training in multi-exit networks, improving robustness and accuracy.

NEO-KD enhances adversarial training by optimizing knowledge distillation in multi-exit networks through careful tuning of hyperparameters.

Tuning hyperparameters α and β in NEO-KD is crucial for balancing knowledge transfer and maintaining adversarial training effectiveness.