Post-training Model Quantization Using GANs for Synthetic Data
  Generation
Athanasios Masouris Mansi Sharma Adrian Boguszewski Alexander Kozlov Zhuo Wu Raymond Lo
Quantization is a widely adopted technique for deep neural networks to reduce
the memory and computational resources required. However, when quantized, most
models would need a suitable calibration process to keep their performance
intact, which requires data from the target domain, such as a fraction of the
dataset used in model training and model validation (i.e. calibration dataset).
  In this study, we investigate the use of synthetic data as a substitute for
the calibration with real data for the quantization method. We propose a data
generation method based on Generative Adversarial Networks that are trained
prior to the model quantization step. We compare the performance of models
quantized using data generated by StyleGAN2-ADA and our pre-trained DiStyleGAN,
with quantization using real data and an alternative data generation method
based on fractal images. Overall, the results of our experiments demonstrate
the potential of leveraging synthetic data for calibration during the
quantization process. In our experiments, the percentage of accuracy
degradation of the selected models was less than 0.6%, with our best
performance achieved on MobileNetV2 (0.05%). The code is available at:
https://github.com/ThanosM97/gsoc2022-openvino