Tag: data-augmentation (5 references)
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
Generalizes Data Shapley using Beta weighting functions, providing noise-reduced data valuation that better handles outliers and mislabeled data detection.
A Survey on Image Data Augmentation for Deep Learning
Comprehensive survey of image data augmentation techniques for deep learning, covering geometric transformations, color space transforms, kernel filters, mixing images, random erasing, and neural style transfer approaches.
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Combines cutting and mixing: patches from one image replace regions in another, with labels mixed proportionally. Improves over Cutout by using cut pixels constructively rather than zeroing them out.
mixup: Beyond Empirical Risk Minimization
Introduces mixup, a data augmentation technique that trains on convex combinations of input pairs and their labels. Simple, data-independent, and model-agnostic approach that improves generalization and robustness.
Improved Regularization of Convolutional Neural Networks with Cutout
Introduces Cutout, a regularization technique that randomly masks square regions of input images during training. Inspired by dropout but applied to inputs, encouraging models to learn from partially visible objects.