Not really. Well, sort of. If you have a small dataset, you can use data augmentation to "stretch" the dataset into a larger size. It's unknown whether data augmentation helps in general though (i.e. when you have lots of training data).
An augmentation is color dropout (photo becomes greyscale), cutmix (splicing two different training examples together), blur, noise, yada yada.
An augmentation is color dropout (photo becomes greyscale), cutmix (splicing two different training examples together), blur, noise, yada yada.