Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
This addresses the need for accurate regression models in scenarios with limited labeled data, though it appears incremental as it builds on existing regression and semi-supervised concepts.
The paper tackles the problem of semi-supervised regression by introducing Twin Neural Network Regression (TNNR), which predicts differences between target values and uses unlabeled data with labeled anchors, resulting in state-of-the-art performance with significant improvements.
Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present. TNNR is trained to predict differences between the target values of two different data points rather than the targets themselves. By ensembling predicted differences between the targets of an unseen data point and all training data points, it is possible to obtain a very accurate prediction for the original regression problem. Since any loop of predicted differences should sum to zero, loops can be supplied to the training data, even if the data points themselves within loops are unlabelled. Semi-supervised training improves TNNR performance, which is already state of the art, significantly.