


Figure 9. Quantizing a solution leads to a perturbation of the weights which has a greater effect on the quality of the sharp solution (left) compared to wide solution (right).  



Figure 10. The difference between standard low precision training and SWALP.
Another work, SQWA, presents an approach for quantization and fine-tuning of neural networks in low precision [12]. In particular, SQWA achieved state-of-the-art results for DNNs quantized to 2 bits on CIFAR-100 and ImageNet.
Calibration and Uncertainty Estimates
By finding a centred solution in the loss, SWA can also improve calibration and uncertainty representation. Indeed, SWA can be viewed as an approximation to an ensemble, resembling a Bayesian model average, but with a single model [1].