How can Stochastic Weight Averaging SWA improve model generalization

Question

With the help of code can you tell me How can Stochastic Weight Averaging (SWA) improve model generalization?

Answer 1

You can improve model generalization using Stochastic Weight Averaging (SWA) by averaging weights from multiple points along the training trajectory to find flatter minima.
Here is the code snippet below:

In the above code we are using the following key points:

• AveragedModel accumulates weights over training to capture flatter optima

• SWALR schedules learning rate appropriate for SWA

• update_bn updates batch normalization statistics after weight averaging

Hence, SWA helps models generalize better by navigating toward flat loss surfaces that are less sensitive to input variations.