Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

ArXi:2605.09697v1 Announce Type: cross In many real-world computer vision applications, including medical imaging and industrial inspection, binary classification tasks are characterized by a severe scarcity of positive samples. A widely adopted solution is to generate synthetic positive data using image-to-image transformations applied to negative samples.