  In the field of computer vision, data augmentation is widely used to enrich
the feature complexity of training datasets with deep learning techniques.
However, regarding the generalization capabilities of models, the difference in
artificial features generated by data augmentation and natural visual features
has not been fully revealed. This study introduces the concept of "visual
representation variables" to define the possible visual variations in a task as
a joint distribution of these variables. We focus on the visual representation
variable "illumination", by simulating its distribution degradation and
examining how data augmentation techniques enhance model performance on a
classification task. Our goal is to investigate the differences in
generalization between models trained with augmented data and those trained
under real-world illumination conditions. Results indicate that after applying
various data augmentation methods, model performance has significantly
improved. Yet, a noticeable generalization gap still exists after utilizing
various data augmentation methods, emphasizing the critical role of feature
diversity in the training set for enhancing model generalization.
