Method and apparatus for data-free post-training network quantization and generating synthetic data based on a pre-trained machine learning model

Abstract

A method for training a generator, by a generator training system including a processor and memory, includes: extracting training statistical characteristics from a batch normalization layer of a pre-trained model, the training statistical characteristics including a training mean μ and a training variance σ; initializing a generator configured with generator parameters; generating a batch of synthetic data using the generator; supplying the batch of synthetic data to the pre-trained model; measuring statistical characteristics of activations at the batch normalization layer and at the output of the pre-trained model in response to the batch of synthetic data, the statistical characteristics including a measured mean {circumflex over (μ)}and a measured variance {circumflex over (σ)}; computing a training loss in accordance with a loss function Lbased on μ, σ, {circumflex over (μ)}, and {circumflex over (σ)}; and iteratively updating the generator parameters in accordance with the training loss until a training completion condition is met to compute the generator.