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Training convolutional networks for semantic segmentation with strong(per-pixel) and weak (per-bounding-box) supervision requires a large amount ofweakly labeled data. We propose two methods for selecting the most relevantdata with weak supervision. The first method is designed for finding visuallysimilar images without the need of labels and is based on modeling imagerepresentations with a Gaussian Mixture Model (GMM). As a byproduct of GMMmodeling, we present useful insights on characterizing the data generatingdistribution. The second method aims at finding images with high objectdiversity and requires only the bounding box labels. Both methods are developedin the context of automated driving and experimentation is conducted onCityscapes and Open Images datasets. We demonstrate performance gains byreducing the amount of employed weakly labeled images up to 100 times for OpenImages and up to 20 times for Cityscapes.

Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision