# Dataset Augmentation with Synthetic Images Improves Semantic Segmentation Source: [Dataset Augmentation with Synthetic Images Improves Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:05:03.439Z Authors: Manik Goyal, Param Rajpura, Hristo Bojinov, Ravi Hegde Published: 2017-09-04T07:58:59.000Z ## Abstract Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts required for the creation of training data remains a roadblock for further improvements. We show that augmentation of the weakly annotated training dataset with synthetic images minimizes both the annotation efforts and also the cost of capturing images with sufficient variety. Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our approach is thus a promising solution to the problems of annotation and dataset collection. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()