Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation

By Gaurav Kumar Nayak, Konda Reddy Mopuri, Anirban Chakraborty. 2020

22 citations recorded by the source corpus.

Knowledge Distillation is an effective method to transfer the learning across deep neural networks. Typically, the dataset originally used for training the Teacher model is chosen as the "Transfer Set" to conduct the knowledge transfer to the Student. However, this original training data may not always be freely available due to privacy or sensitivity concerns. In such scenarios, existing approaches either iteratively compose a synthetic set representative of the original training dataset, one sample at a time or learn a generative model to compose such a transfer set. However, both these approaches involve complex optimization (GAN training or several backpropagation steps to synthesize one sample) and are often computationally expensive. In this paper, as a simple alternative, we investigate the effectiveness of "arbitrary transfer sets" such as random noise, publicly available synthetic, and natural datasets, all of which are completely unrelated to the original training dataset in terms of their visual or semantic contents. Through extensive experiments on multiple benchmark datasets such as MNIST, FMNIST, CIFAR-10 and CIFAR-100, we discover and validate surprising effectiveness of using arbitrary data to conduct knowledge distillation when this dataset is "target-class balanced". We believe that this important observation can potentially lead to designing base-lines for the data-free knowledge distillation task.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Knowledge Distillation: A Survey (2020, background): “…have been proposed to overcome problems with unavailable data arising from privacy, legality, security and conﬁdentiality concerns (Chen et al. 2019a; Lopes et al. 2017; Nayak et al. 2019; Micaelli and Storkey 2019; Haroush et al. 2020; Ye et al. 2020; Nayak et al. 2021; Chawla et al. 2021).”