In this paper we introduce a model of lifelong learning, based on a Network
of Experts. New tasks / experts are learned and added to the model
sequentially, building on what was learned before. To ensure scalability of
this process,data from previous tasks cannot be stored and hence is not
available when learning a new task. A critical issue in such context, not
addressed in the literature so far, relates to the decision which expert to
deploy at test time. We introduce a set of gating autoencoders that learn a
representation for the task at hand, and, at test time, automatically forward
the test sample to the relevant expert. This also brings memory efficiency as
only one expert network has to be loaded into memory at any given time.
Further, the autoencoders inherently capture the relatedness of one task to
another, based on which the most relevant prior model to be used for training a
new expert, with finetuning or learning without-forgetting, can be selected. We
evaluate our method on image classification and video prediction problems.

本文介绍了一种基于 Expert 网络的终身学习模型，引入了一组门控自编码器在测试时将样本自动转发到相关专家，从而具有内在的内存效率和任务相关性。并使用图像分类和视频预测问题进行了实验评估。