Impact of Regularization on Calibration and Robustness: from the Representation Space Perspective

Abstract

Recent studies have shown that regularization techniques using soft labels, e.g., label smoothing, Mixup, and CutMix, not only enhance image classification accuracy but also mitigate miscalibration due to overconfident predictions, and improve robustness against adversarial attacks. However, the underlying mechanisms of such improvements remain underexplored. In this paper, we offer a novel explanation from the perspective of the representation space (i.e., the space of the features obtained at the penultimate layer). Based on examination of decision boundaries and structure of features (or representation vectors), our study investigates confidence contours and gradient directions within the representation space. Furthermore, we analyze the adjustments in feature distributions due to regularization in relation to these contours and directions, from which we uncover central mechanisms inducing improved calibration and robustness. Our findings provide new insights into the characteristics of the high-dimensional representation space in relation to training and regularization using soft labels.