Corpus ID: 208637294

Deep Ensembles: A Loss Landscape Perspective

@article{Fort2019DeepEA,
  title={Deep Ensembles: A Loss Landscape Perspective},
  author={Stanislav Fort and Huiyi Hu and Balaji Lakshminarayanan},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.02757}
}
Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep learning models. While deep ensembles were theoretically motivated by the bootstrap, non-bootstrap ensembles trained with just random initialization also perform well in practice, which suggests that there could be other explanations for why deep ensembles work well. Bayesian neural networks, which learn distributions over the parameters of the… Expand
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