The loss function plays an important role in optimizing the performance of a learning system. A crucial aspect of the loss function is the assignment of sample weights within a mini-batch during loss computation. In the context of continual learning (CL), most existing strategies uniformly treat samples when calculating the loss value, thereby assigning equal weights to each sample. While this approach can be effective in certain standard benchmarks, its optimal effectiveness, particularly in more complex scenarios, remains underexplored. This is particularly pertinent in training "in the wild," such as with self-training, where labeling is automated using a reference model. This paper introduces the Online Meta-learning for Sample Importance (OMSI) strategy that approximates sample weights for a mini-batch in an online CL stream using an inner- and meta-update mechanism. This is done by first estimating sample weight parameters for each sample in the mini-batch, then, updating the model with the adapted sample weights. We evaluate OMSI in two distinct experimental settings. First, we show that OMSI enhances both learning and retained accuracy in a controlled noisy-labeled data stream. Then, we test the strategy in three standard benchmarks and compare it with other popular replay-based strategies. This research aims to foster the ongoing exploration in the area of self-adaptive CL.

翻译：损失函数在优化学习系统性能中扮演着重要角色。损失函数的一个关键方面是在计算损失时对迷你批次内样本权重的分配。在持续学习（CL）背景下，大多数现有策略在计算损失值时统一处理样本，从而为每个样本赋予相同权重。虽然这种方法在某些标准基准测试中可能有效，但其在更复杂场景下的最优有效性仍未得到充分探索。这一点在"野外"训练中尤为重要，例如使用参考模型自动标注的自训练场景。本文提出在线元学习样本重要性（OMSI）策略，该策略通过内更新和元更新机制，在在线持续学习流中近似估计迷你批次内样本的权重。具体而言，首先估算迷你批次中每个样本的权重参数，然后使用调整后的样本权重更新模型。我们在两种不同的实验设置下评估OMSI。首先，我们证明在受控的噪声标注数据流中，OMSI能同时提升学习准确率和保留准确率。随后，在三个标准基准测试中测试该策略，并将其与其他流行的基于回放的策略进行比较。本研究旨在推动自适应持续学习领域的持续探索。