Despite much research targeted at enabling conventional machine learning models to continually learn tasks and data distributions sequentially without forgetting the knowledge acquired, little effort has been devoted to account for more realistic situations where learning some tasks accurately might be more critical than forgetting previous ones. In this paper we propose a Bayesian inference based framework to continually learn a set of real-world, sensing-based analysis tasks that can be tuned to prioritize the remembering of previously learned tasks or the learning of new ones. Our experiments prove the robustness and reliability of the learned models to adapt to the changing sensing environment, and show the suitability of using uncertainty of the predictions to assess their reliability.

中文翻译：

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当我们不知道时知道：基于传感的分析任务的贝叶斯持续学习

尽管许多研究旨在使传统的机器学习模型能够连续学习任务和数据分布而不会忘记所获得的知识，但很少有人致力于解释更现实的情况，在这些情况下，准确地学习某些任务可能比忘记以前的任务更重要。在本文中，我们提出了一个基于贝叶斯推理的框架，以不断学习一组现实世界的、基于传感的分析任务，这些任务可以调整为优先记住以前学习的任务或学习新任务。我们的实验证明了学习模型的鲁棒性和可靠性，以适应不断变化的传感环境，并展示了使用预测的不确定性来评估其可靠性的适用性。