The goal of lifelong learning is to continuously learn from non-stationary distributions, where the non-stationarity is typically imposed by a sequence of distinct tasks. Prior works have mostly considered idealistic settings, where the identity of tasks is known at least at training. In this paper we focus on a fundamentally harder, so-called task-agnostic setting where the task identities are not known and the learning machine needs to infer them from the observations. Our algorithm, which we call TAME (Task-Agnostic continual learning using Multiple Experts), automatically detects the shift in data distributions and switches between task expert networks in an online manner. At training, the strategy for switching between tasks hinges on an extremely simple observation that for each new coming task there occurs a statistically-significant deviation in the value of the loss function that marks the onset of this new task. At inference, the switching between experts is governed by the selector network that forwards the test sample to its relevant expert network. The selector network is trained on a small subset of data drawn uniformly at random. We control the growth of the task expert networks as well as selector network by employing online pruning. Our experimental results show the efficacy of our approach on benchmark continual learning data sets, outperforming the previous task-agnostic methods and even the techniques that admit task identities at both training and testing, while at the same time using a comparable model size.

翻译：终身学习的目标是从非平稳分布中持续学习，这种非平稳性通常由一系列不同任务所引入。先前的研究大多考虑理想化场景，即在训练时至少已知任务身份。本文聚焦于一种本质上更困难的、所谓任务无关的场景，其中任务身份未知，学习机器需要从观测中推断它们。我们提出的算法称为TAME（基于多专家的任务无关持续学习），能自动检测数据分布的偏移，并以在线方式在任务专家网络间切换。在训练时，任务切换策略基于一个极其简单的观察：对于每个新到来的任务，损失函数值会出现统计显著的偏离，这标志着新任务的开始。在推理时，专家间的切换由选择器网络控制，该网络将测试样本分配给其相关的专家网络。选择器网络在从数据中均匀随机抽取的小规模子集上进行训练。我们通过在线剪枝控制任务专家网络及选择器网络的规模增长。实验结果表明，我们的方法在基准持续学习数据集上表现优异，不仅超越了先前的任务无关方法，甚至优于那些在训练和测试阶段均已知任务身份的技术，同时保持了可比的模型规模。