Zero-Shot Knowledge Distillation Using Label-Free Adversarial Perturbation With Taylor Approximation

Knowledge distillation (KD) is one of the most effective neural network light-weighting techniques when training data is available. However, KD is seldom applicable to an environment where it is difficult or impossible to access training data. To solve this problem, a complete zero-shot KD (C-ZSKD) based on adversarial learning has been recently proposed, but the so-called biased sample generation problem limits the performance of C-ZSKD. To...