Evaluating NEO-KD Against Single-Exit Defense Methods in Multi-Exit Networks | HackerNoonNEO-KD algorithm is superior for adversarial defense in multi-exit networks compared to adapted single-exit strategies.
Examining the Adversarial Test Accuracy of Later Exits in NEO-KD Networks | HackerNoonLater exits in NEO-KD show reduced adversarial test accuracy due to higher cumulative losses.Ensemble strategies can improve performance at later exits by addressing adversarial challenges.
Evaluating NEO-KD Against Single-Exit Defense Methods in Multi-Exit Networks | HackerNoonNEO-KD algorithm is superior for adversarial defense in multi-exit networks compared to adapted single-exit strategies.
Examining the Adversarial Test Accuracy of Later Exits in NEO-KD Networks | HackerNoonLater exits in NEO-KD show reduced adversarial test accuracy due to higher cumulative losses.Ensemble strategies can improve performance at later exits by addressing adversarial challenges.
Comparison with SKD and ARD and Implementations of Stronger Attacker Algorithms | HackerNoonNEO-KD significantly enhances the performance of multi-exit neural networks, especially during adversarial training, compared to traditional self-distillation techniques.
Clean Test Accuracy and Adversarial Training via Average Attack | HackerNoonNEO-KD offers competitive clean accuracy while enhancing adversarial accuracy, especially on Tiny-ImageNet.
Comparison with SKD and ARD and Implementations of Stronger Attacker Algorithms | HackerNoonNEO-KD significantly enhances the performance of multi-exit neural networks, especially during adversarial training, compared to traditional self-distillation techniques.
Clean Test Accuracy and Adversarial Training via Average Attack | HackerNoonNEO-KD offers competitive clean accuracy while enhancing adversarial accuracy, especially on Tiny-ImageNet.