#knowledge-distillation

#knowledge-distillation

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.

Gemma 2 by Google DeepMind outperforms models of comparable size, incorporates Gemini architecture ideas, and uses knowledge distillation for improved performance.