Experiments

The DiStyleGAN model, developed at the first part of this project, generates images from the distribution of the CIFAR-10 dataset. Therefore, we experimented with PyTorch models, pre-trained on CIFAR-10 for classification. In our experiments, we used both the Default Quantization and the Accuracy-control quantization methods provided by the OpenVINO Post-training Optimization Tool. The same experiments were conducted using multiple calibration datasets, and we compared the results of the quantized models for the classification task on the official CIFAR-10 test set.