It was an image segmentation task, and the features were similar across data sets. The other thing that made it work well was heavy use of data augmentation tha

It was an image segmentation task, and the features were similar across data sets. The other thing that made it work well was heavy use of data augmentation that captured ways in which different data points could reasonably differ. There was a really cool medical imaging paper recently that literally just labeled several 2D slices in a 3D dataset consisting of 3 images and performed a reasonable segmentation: https://arxiv.org/abs/1606.06650 https://arxiv.org/abs/1606.06650