  While there has been remarkable progress in the performance of visual
recognition algorithms, the state-of-the-art models tend to be exceptionally
data-hungry. Large labeled training datasets, expensive and tedious to produce,
are required to optimize millions of parameters in deep network models. Lagging
behind the growth in model capacity, the available datasets are quickly
becoming outdated in terms of size and density. To circumvent this bottleneck,
we propose to amplify human effort through a partially automated labeling
scheme, leveraging deep learning with humans in the loop. Starting from a large
set of candidate images for each category, we iteratively sample a subset, ask
people to label them, classify the others with a trained model, split the set
into positives, negatives, and unlabeled based on the classification
confidence, and then iterate with the unlabeled set. To assess the
effectiveness of this cascading procedure and enable further progress in visual
recognition research, we construct a new image dataset, LSUN. It contains
around one million labeled images for each of 10 scene categories and 20 object
categories. We experiment with training popular convolutional networks and find
that they achieve substantial performance gains when trained on this dataset.
