  In this paper, we examine the overfitting behavior of image classification
models modified with Implicit Background Estimation (SCrIBE), which transforms
them into weakly supervised segmentation models that provide spatial domain
visualizations without affecting performance. Using the segmentation masks, we
derive an overfit detection criterion that does not require testing labels. In
addition, we assess the change in model performance, calibration, and
segmentation masks after applying data augmentations as overfitting reduction
measures and testing on various types of distorted images.
