How do CNNs generalize across different image distributions?

CNNs generalize using learned hierarchical features, data augmentation, and regularization techniques that reduce overfitting to training distribution.

CNNs generalize by learning invariant features such as edges, shapes, and textures that appear across datasets. Techniques like dropout, batch normalization, and data augmentation improve robustness. However, CNNs can still struggle with domain shift, where training and test distributions differ significantly, requiring domain adaptation methods.