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Lsun
Classification
Scenario Recognition
|...
License: Unknown

Overview

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becoming outdated in terms of size and density.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.

Citation

Please use the following citation when referencing the dataset:

@article{yu15lsun,
    Author = {Yu, Fisher and Zhang, Yinda and Song, Shuran and Seff, Ari and Xiao, Jianxiong},
    Title = {LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans
in the Loop},
    Journal = {arXiv preprint arXiv:1506.03365},
    Year = {2015}
}
Data Summary
Type
Image,
Amount
--
Size
156.55GB
Provided by
Princeton University
Princeton is about people. Our University is enriched by the wide range of experiences and perspectives of our students, faculty, staff and alumni.
| Amount -- | Size 156.55GB
Lsun
Classification
Scenario Recognition
License: Unknown

Overview

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.

Citation

Please use the following citation when referencing the dataset:

@article{yu15lsun,
    Author = {Yu, Fisher and Zhang, Yinda and Song, Shuran and Seff, Ari and Xiao, Jianxiong},
    Title = {LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans
in the Loop},
    Journal = {arXiv preprint arXiv:1506.03365},
    Year = {2015}
}
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