  Online continual learning aims to continuously train neural networks from a
continuous data stream with a single pass-through data. As the most effective
approach, the rehearsal-based methods replay part of previous data. Commonly
used predictors in existing methods tend to generate biased dot-product logits
that prefer to the classes of current data, which is known as a bias issue and
a phenomenon of forgetting. Many approaches have been proposed to overcome the
forgetting problem by correcting the bias; however, they still need to be
improved in online fashion. In this paper, we try to address the bias issue by
a more straightforward and more efficient method. By decomposing the
dot-product logits into an angle factor and a norm factor, we empirically find
that the bias problem mainly occurs in the angle factor, which can be used to
learn novel knowledge as cosine logits. On the contrary, the norm factor
abandoned by existing methods helps remember historical knowledge. Based on
this observation, we intuitively propose to leverage the norm factor to balance
the new and old knowledge for addressing the bias. To this end, we develop a
heuristic approach called unbias experience replay (UER). UER learns current
samples only by the angle factor and further replays previous samples by both
the norm and angle factors. Extensive experiments on three datasets show that
UER achieves superior performance over various state-of-the-art methods. The
code is in https://github.com/FelixHuiweiLin/UER.
