#adversarial-training
#adversarial-training

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#multi-exit-networks

Comparison with SKD and ARD and Implementations of Stronger Attacker Algorithms | HackerNoon

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

Evaluating NEO-KD Against Single-Exit Defense Methods in Multi-Exit Networks | HackerNoon

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

Fine-Tuning NEO-KD for Robust Multi-Exit Networks | HackerNoon

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

The Impact of Hyperparameters on Adversarial Training Performance | HackerNoon

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.

Comparison with SKD and ARD and Implementations of Stronger Attacker Algorithms | HackerNoon

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

Evaluating NEO-KD Against Single-Exit Defense Methods in Multi-Exit Networks | HackerNoon

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

Fine-Tuning NEO-KD for Robust Multi-Exit Networks | HackerNoon

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

The Impact of Hyperparameters on Adversarial Training Performance | HackerNoon

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.

moremulti-exit-networks

Clean Test Accuracy and Adversarial Training via Average Attack | HackerNoon

NEO-KD offers competitive clean accuracy while enhancing adversarial accuracy, especially on Tiny-ImageNet.

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