A significant challenge in achieving ubiquitous Artificial Intelligence is
the limited ability of models to rapidly learn new information in real-world
scenarios where data follows long-tailed distributions, all while avoiding
forgetting previously acquired knowledge. In this work, we study the
under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which
aims to learn new tasks from sequentially arriving class-imbalanced data
streams. Each data is observed only once for training without knowing the task
data distribution. We present DELTA, a decoupled learning approach designed to
enhance learning representations and address the substantial imbalance in
LTOCL. We enhance the learning process by adapting supervised contrastive
learning to attract similar samples and repel dissimilar (out-of-class)
samples. Further, by balancing gradients during training using an equalization
loss, DELTA significantly enhances learning outcomes and successfully mitigates
catastrophic forgetting. Through extensive evaluation, we demonstrate that
DELTA improves the capacity for incremental learning, surpassing existing OCL
methods. Our results suggest considerable promise for applying OCL in
real-world applications.

DELTA 是一种增强学习表示、解决长尾在线连续学习中类别不平衡问题的分离式学习方法，通过适应性有监督对比学习吸引相似样本和排斥不相似（非类内）样本，平衡训练期间的梯度并通过等化损失显著提升学习结果，并成功缓解了灾难性遗忘，扩展了增量学习的能力，超越了现有的 OCL 方法，对实际应用具有重大的潜在价值。