While going over PyTorch image augmentations, https://pytorch.org/vision/stable/transforms.html, I see that some augmentations can be applied with a certain probability. What is the purpose of applying stochastic augmentations rather than consistently applying a certain augmentation?
Stochastic augmentations are used to sample from all possible augmentations, when the dataset size of all possible augmentations would be too large.
This is usually done on-demand, and as a result each training epoch should result in slighty different inputs. This approach can be beneficial in preventing overfitting when the variability in possible inputs is far larger than you could ever properly sample from (e.g. natural images).