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Ensembles, where multiple neural networks are trained individually and theirpredictions are averaged, have been shown to be widely successful for improvingboth the accuracy and predictive uncertainty of single neural networks.However, an ensemble's cost for both training and testing increases linearlywith the number of networks, which quickly becomes untenable.  In this paper, we propose BatchEnsemble, an ensemble method whosecomputational and memory costs are significantly lower than typical ensembles.BatchEnsemble achieves this by defining each weight matrix to be the Hadamardproduct of a shared weight among all ensemble members and a rank-one matrix permember. Unlike ensembles, BatchEnsemble is not only parallelizable acrossdevices, where one device trains one member, but also parallelizable within adevice, where multiple ensemble members are updated simultaneously for a givenmini-batch. Across CIFAR-10, CIFAR-100, WMT14 EN-DE/EN-FR translation, andout-of-distribution tasks, BatchEnsemble yields competitive accuracy anduncertainties as typical ensembles; the speedup at test time is 3X and memoryreduction is 3X at an ensemble of size 4. We also apply BatchEnsemble tolifelong learning, where on Split-CIFAR-100, BatchEnsemble yields comparableperformance to progressive neural networks while having a much lowercomputational and memory costs. We further show that BatchEnsemble can easilyscale up to lifelong learning on Split-ImageNet which involves 100 sequentiallearning tasks.

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning