Learning from non-stationary data streams, also called Task-Free Continual
Learning (TFCL) remains challenging due to the absence of explicit task
information. Although recently some methods have been proposed for TFCL, they
lack theoretical guarantees. Moreover, forgetting analysis during TFCL was not
studied theoretically before. This paper develops a new theoretical analysis
framework which provides generalization bounds based on the discrepancy
distance between the visited samples and the entire information made available
for training the model. This analysis gives new insights into the forgetting
behaviour in classification tasks. Inspired by this theoretical model, we
propose a new approach enabled by the dynamic component expansion mechanism for
a mixture model, namely the Online Discrepancy Distance Learning (ODDL). ODDL
estimates the discrepancy between the probabilistic representation of the
current memory buffer and the already accumulated knowledge and uses it as the
expansion signal to ensure a compact network architecture with optimal
performance. We then propose a new sample selection approach that selectively
stores the most relevant samples into the memory buffer through the
discrepancy-based measure, further improving the performance. We perform
several TFCL experiments with the proposed methodology, which demonstrate that
the proposed approach achieves the state of the art performance.

本文提出了一种新的理论分析框架，给出了基于任务自由连续学习中访问样本与整个信息之间差异距离的归纳界限，并提出了使用动态组件扩展机制的新方法 - ODDL，以保证具有最佳性能的紧凑网络结构，同时提出了一种基于差异度量的样本选择方法，通过提高性能证明了该方法的卓越表现。

通过在线差异距离学习实现无任务持续学习

Task-Free Continual Learning via Online Discrepancy Distance Learning

Recently, continual learning (CL) has gained significant interest because it
enables deep learning models to acquire new knowledge without forgetting
previously learnt information. However, most existing works require knowing the
task identities and boundaries, which is not realistic in a real context. In
this paper, we address a more challenging and realistic setting in CL, namely
the Task-Free Continual Learning (TFCL) in which a model is trained on
non-stationary data streams with no explicit task information. To address TFCL,
we introduce an evolved mixture model whose network architecture is dynamically
expanded to adapt to the data distribution shift. We implement this expansion
mechanism by evaluating the probability distance between the knowledge stored
in each mixture model component and the current memory buffer using the Hilbert
Schmidt Independence Criterion (HSIC). We further introduce two simple dropout
mechanisms to selectively remove stored examples in order to avoid memory
overload while preserving memory diversity. Empirical results demonstrate that
the proposed approach achieves excellent performance.

本文提出了一种解决深度学习模型在没有显式任务信息的非定常数据流上进行持续学习的方法，即基于进化的混合模型，该模型的网络架构动态扩展以适应数据分布的变化，并引入了两个简单的丢弃机制来避免存储的例子过多导致记忆过载问题，实证结果表明，该方法在持续学习任务上取得了优异的性能。

学习一种进化的混合模型用于任务自由的连续学习

Learning an evolved mixture model for task-free continual learning

We explore task-free continual learning (CL), in which a model is trained to
avoid catastrophic forgetting in the absence of explicit task boundaries or
identities. Among many efforts on task-free CL, a notable family of approaches
are memory-based that store and replay a subset of training examples. However,
the utility of stored seen examples may diminish over time since CL models are
continually updated. Here, we propose Gradient based Memory EDiting (GMED), a
framework for editing stored examples in continuous input space via gradient
updates, in order to create more "challenging" examples for replay. GMED-edited
examples remain similar to their unedited forms, but can yield increased loss
in the upcoming model updates, thereby making the future replays more effective
in overcoming catastrophic forgetting. By construction, GMED can be seamlessly
applied in conjunction with other memory-based CL algorithms to bring further
improvement. Experiments validate the effectiveness of GMED, and our best
method significantly outperforms baselines and previous state-of-the-art on
five out of six datasets. Code can be found at this https URL

本文提出了一种名为 Gradient based Memory EDiting (GMED) 的基于梯度更新的框架，以在连续输入空间中编辑存储的示例，旨在为回放创建更具挑战性的示例，以克服灾难性遗忘，并验证了该方法的有效性，是一种新颖的无任务边界或标识的任务自由继续学习方法。