Deep Ordinal

Deep ordinal methods focus on improving the accuracy and interpretability of machine learning models for ordinal data—data with inherent order but not necessarily equal intervals between categories. Current research emphasizes developing novel loss functions, output layers, and training strategies within deep learning frameworks like convolutional neural networks and residual networks to better capture ordinal relationships, often incorporating techniques like constrained proxy learning or ordinal regression. These advancements are impacting diverse fields, from improving 3D hand pose estimation and image classification to supporting risk assessment in public health by analyzing remote sensing data. The development of comprehensive software packages further facilitates broader adoption and application of these techniques.