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While there has been remarkable progress in the performance of visualrecognition algorithms, the state-of-the-art models tend to be exceptionallydata-hungry. Large labeled training datasets, expensive and tedious to produce,are required to optimize millions of parameters in deep network models. Laggingbehind the growth in model capacity, the available datasets are quicklybecoming outdated in terms of size and density. To circumvent this bottleneck,we propose to amplify human effort through a partially automated labelingscheme, leveraging deep learning with humans in the loop. Starting from a largeset of candidate images for each category, we iteratively sample a subset, askpeople to label them, classify the others with a trained model, split the setinto positives, negatives, and unlabeled based on the classificationconfidence, and then iterate with the unlabeled set. To assess theeffectiveness of this cascading procedure and enable further progress in visualrecognition research, we construct a new image dataset, LSUN. It containsaround one million labeled images for each of 10 scene categories and 20 objectcategories. We experiment with training popular convolutional networks and findthat they achieve substantial performance gains when trained on this dataset.

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop