Improved Regularization of Convolutional Neural Networks with Cutout
Fields with differences: venue. Compare local vs external BibTeX below.
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arXiv preprint
Abstract
Introduces Cutout, a regularization technique that randomly masks square regions of input images during training. Inspired by dropout but applied to inputs, encouraging models to learn from partially visible objects.
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@article{devries2017cutout,
title = {Improved Regularization of Convolutional Neural Networks with Cutout},
author = {Terrance DeVries and Graham W. Taylor},
year = {2017},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1708.04552},
abstract = {Introduces Cutout, a regularization technique that randomly masks square regions of input images during training. Inspired by dropout but applied to inputs, encouraging models to learn from partially visible objects.}
} From AUTO:OPENALEX
@article{devries2017cutout,
title = {Improved Regularization of Convolutional Neural Networks with Cutout},
author = {Terrance DeVries and Graham W. Taylor},
year = {2017},
journal = {arXiv (Cornell University)},
doi = {10.48550/arxiv.1708.04552}
}