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Register a new userRobustness and Overfitting Behavior of Implicit Background Models
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Shirley Liu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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