Web: http://arxiv.org/abs/2201.10908

Jan. 27, 2022, 2:10 a.m. | Hendrik Alexander Mehrtens, Camila González, Anirban Mukhopadhyay

cs.LG updates on arXiv.org arxiv.org

Calibration and uncertainty estimation are crucial topics in high-risk
environments. We introduce a new diversity regularizer for classification tasks
that uses out-of-distribution samples and increases the overall accuracy,
calibration and out-of-distribution detection capabilities of ensembles.
Following the recent interest in the diversity of ensembles, we systematically
evaluate the viability of explicitly regularizing ensemble diversity to improve
calibration on in-distribution data as well as under dataset shift. We
demonstrate that diversity regularization is highly beneficial in
architectures, where weights are partially …

arxiv diversity

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