Data-Free Knowledge Distillation for Deep Neural Networks

Undergraduate research on data-free model compression.
Poster presented at ML@GT Spring 2017 Symposium.
Paper presented at NIPS 2017 LLD Workshop.
Under review at AISTATS 2018.
Pre-print on arxiv now!
Source code available right now!

POSTER
     
ARXIV
     
SOURCE CODE

ABSTRACT

Recent advances in model compression have provided procedures for compressing large neural networks to a fraction of their original size while retaining most if not all of their accuracy. However, all of these approaches rely on access to the original training set, which might not always be possible if the network to be compressed was trained on a very large dataset, or on a dataset whose release poses privacy or safety concerns as may be the case for biometrics tasks. We present a method for data-free knowledge distillation, which is able to compress deep neural networks trained on large-scale datasets to a fraction of their size leveraging only some extra metadata to be provided with a pretrained model release. We also explore different kinds of metadata that can be used with our method, and discuss tradeoffs involved in using each of them.


AUTHORS

- Raphael Gontijo Lopes
- Stefano Fenu
- Thad Starner