  Online continual learning in the wild is a very difficult task in machine
learning. Non-stationarity in online continual learning potentially brings
about catastrophic forgetting in neural networks. Specifically, online
continual learning for autonomous driving with SODA10M dataset exhibits extra
problems on extremely long-tailed distribution with continuous distribution
shift. To address these problems, we propose multiple deep metric
representation learning via both contrastive and supervised contrastive
learning alongside soft labels distillation to improve model generalization.
Moreover, we exploit modified class-balanced focal loss for sensitive
penalization in class imbalanced and hard-easy samples. We also store some
samples under guidance of uncertainty metric for rehearsal and perform online
and periodical memory updates. Our proposed method achieves considerable
generalization with average mean class accuracy (AMCA) 64.01% on validation and
64.53% AMCA on test set.
