  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.
