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

@article{Yu2015LSUNCO,
  title={LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop},
  author={Fisher Yu and Yinda Zhang and Shuran Song and Ari Seff and Jianxiong Xiao},
  journal={ArXiv},
  year={2015},
  volume={abs/1506.03365}
}
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 Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density.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… CONTINUE READING

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  • Our procedure uses statistical tests to ensure labeling quality, providing more than 90% precision on average according to verification tests.

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