  We explore design principles for general pixel-level prediction problems,
from low-level edge detection to mid-level surface normal estimation to
high-level semantic segmentation. Convolutional predictors, such as the
fully-convolutional network (FCN), have achieved remarkable success by
exploiting the spatial redundancy of neighboring pixels through convolutional
processing. Though computationally efficient, we point out that such approaches
are not statistically efficient during learning precisely because spatial
redundancy limits the information learned from neighboring pixels. We
demonstrate that stratified sampling of pixels allows one to (1) add diversity
during batch updates, speeding up learning; (2) explore complex nonlinear
predictors, improving accuracy; and (3) efficiently train state-of-the-art
models tabula rasa (i.e., "from scratch") for diverse pixel-labeling tasks. Our
single architecture produces state-of-the-art results for semantic segmentation
on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset,
and edge detection on BSDS.
