Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
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发表时间：
2016-12
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通讯作者：
Balaji Lakshminarayanan;A. Pritzel;C. Blundell
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作者：
Balaji Lakshminarayanan;A. Pritzel;C. Blundell
深度神经网络（NNs）是强大的黑匣子预测器，最近在广泛的任务中取得了令人印象深刻的表现。量化神经网络中的预测不确定性是一个具有挑战性且尚未解决的问题。学习权重分布的贝叶斯神经网络是目前估计预测不确定性的最先进方法；然而，这些需要对训练过程进行重大修改，并且与标准（非贝叶斯）神经网络相比，计算成本很高。我们提出了一种替代贝叶斯神经网络的方法，它易于实现，易于并行化，只需要很少的超参数调优，并产生高质量的预测不确定性估计。通过对分类和回归基准的一系列实验，我们证明我们的方法产生了校准良好的不确定性估计，与近似贝叶斯神经网络一样好或更好。为了评估对数据集移位的鲁棒性，我们评估了已知分布和未知分布的测试样例的预测不确定性，并表明我们的方法能够在分布外的样例上表达更高的不确定性。我们通过在ImageNet上评估预测不确定性来证明我们方法的可扩展性。
Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs. We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples. We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet.