Collecting the large datasets needed to train deep neural networks can be
very difficult, particularly for the many applications for which sharing and
pooling data is complicated by practical, ethical, or legal concerns. However,
it may be the case that derivative datasets or predictive models developed
within individual sites can be shared and combined with fewer restrictions.
Training on distributed data and combining the resulting networks is often
viewed as continual learning, but these methods require networks to be trained
sequentially. In this paper, we introduce distributed weight consolidation
(DWC), a continual learning method to consolidate the weights of separate
neural networks, each trained on an independent dataset. We evaluated DWC with
a brain segmentation case study, where we consolidated dilated convolutional
neural networks trained on independent structural magnetic resonance imaging
(sMRI) datasets from different sites. We found that DWC led to increased
performance on test sets from the different sites, while maintaining
generalization performance for a very large and completely independent
multi-site dataset, compared to an ensemble baseline.

介绍了分布式权值整合 (DWC), 它是一种连续学习方法，用于将独立数据集上训练的神经网络的权重整合。通过脑分割案例研究，我们发现 DWC 导致在不同站点的测试集上性能提高，同时与集成基线相比，在非常大且完全独立的多站点数据集上保持泛化性能。