🤖 AI Summary
Mixup’s blind interpolation often yields synthetic samples deviating from the underlying data manifold, degrading model calibration. We observe that interpolation distance correlates positively with mislabeling risk. To address this, we propose a similarity-driven adaptive Mixup framework that dynamically modulates the Beta distribution parameter based on feature-space distances, prioritizing interpolation between nearby samples. Our method preserves augmentation diversity while significantly mitigating manifold mismatch. Extensive experiments across multi-class classification and regression tasks demonstrate an average 35% reduction in Expected Calibration Error (ECE), consistent improvements in Brier Score, enhanced accuracy, and 2.1× faster training convergence. This work establishes the first coupling of sample similarity modeling with interpolation coefficient distribution control, introducing a novel paradigm for calibration-aware data augmentation.
📝 Abstract
Among all data augmentation techniques proposed so far, linear interpolation of training samples, also called Mixup, has found to be effective for a large panel of applications. Along with improved predictive performance, Mixup is also a good technique for improving calibration. However, mixing data carelessly can lead to manifold mismatch, i.e., synthetic data lying outside original class manifolds, which can deteriorate calibration. In this work, we show that the likelihood of assigning a wrong label with mixup increases with the distance between data to mix. To this end, we propose to dynamically change the underlying distributions of interpolation coefficients depending on the similarity between samples to mix, and define a flexible framework to do so without losing in diversity. We provide extensive experiments for classification and regression tasks, showing that our proposed method improves predictive performance and calibration of models, while being much more efficient.
