  When large scale training data is available, one can obtain compact and
accurate networks to be deployed in resource-constrained environments
effectively through quantization and pruning. However, training data are often
protected due to privacy concerns and it is challenging to obtain compact
networks without data. We study data-free quantization and pruning by
transferring knowledge from trained large networks to compact networks.
Auxiliary generators are simultaneously and adversarially trained with the
targeted compact networks to generate synthetic inputs that maximize the
discrepancy between the given large network and its quantized or pruned
version. We show theoretically that the alternating optimization for the
underlying minimax problem converges under mild conditions for pruning and
quantization. Our data-free compact networks achieve competitive accuracy to
networks trained and fine-tuned with training data. Our quantized and pruned
networks achieve good performance while being more compact and lightweight.
Further, we demonstrate that the compact structure and corresponding
initialization from the Lottery Ticket Hypothesis can also help in data-free
training.
