


Figure 4. Train loss and test error along the line connecting the SWA solution (circle) and SGD solution (square). The SWA solution is centered in a wide region of low train loss, while the SGD solution lies near the boundary. Because of the shift between train loss and test error surfaces, the SWA solution leads to much better generalization.
What are the results achieved with SWA?
We release a GitHub repo with examples using the PyTorch implementation of SWA for training DNNs. For example, these examples can be used to achieve the following results on CIFAR-100:
{:.table.table-striped.table-bordered}
 |  | VGG-16 | ResNet-164 | WideResNet-28x10 | 
| ------------- | ------------- |  ------------- |  ------------- |
| SGD | 72.8 ± 0.3 | 78.4 ± 0.3 | 81.0 ± 0.3 | 
| SWA | 74.4 ± 0.3 | 79.8 ± 0.4 | 82.5 ± 0.2 |