Learning a sequence of tasks without access to i.i.d. observations is a
widely studied form of continual learning (CL) that remains challenging. In
principle, Bayesian learning directly applies to this setting, since recursive
and one-off Bayesian updates yield the same result. In practice, however,
recursive updating often leads to poor trade-off solutions across tasks because
approximate inference is necessary for most models of interest. Here, we
describe an alternative Bayesian approach where task-conditioned parameter
distributions are continually inferred from data. We offer a practical deep
learning implementation of our framework based on probabilistic
task-conditioned hypernetworks, an approach we term posterior meta-replay.
Experiments on standard benchmarks show that our probabilistic hypernetworks
compress sequences of posterior parameter distributions with virtually no
forgetting. We obtain considerable performance gains compared to existing
Bayesian CL methods, and identify task inference as our major limiting factor.
This limitation has several causes that are independent of the considered
sequential setting, opening up new avenues for progress in CL.

该研究针对不具有独立同分布观测的连续学习提出了贝叶斯学习的替代方案，即基于概率任务条件化超网络的后验元回放方法，其在标准基准测试中比现有的贝叶斯学习方法表现更好，发现任务推断是其主要限制因素，这一限制有多个原因，与考虑的序列设置无关，为连续学习的进一步发展开辟了新的道路。