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

Understand

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• Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density.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.