Publikation

Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification

Matias Valdenegro-Toro

In: Bayesian Deep Learning Workshop. Neural Information Processing Systems (NIPS-2019) befindet sich NeurIPS 2019 December 8-14 Vancouver BC Canada Keine 12/2019.

Abstrakt

Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea is to ensemble only the layers close to the output. Our results show that this idea enables a trade-off between error and uncertainty quality versus computational performance.

Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence