Incremental Learning (IL) allows AI systems to adapt to streamed data. Most
existing algorithms make two strong hypotheses which reduce the realism of the
incremental scenario: (1) new data are assumed to be readily annotated when
streamed and (2) tests are run with balanced datasets while most real-life
datasets are actually imbalanced. These hypotheses are discarded and the
resulting challenges are tackled with a combination of active and imbalanced
learning. We introduce sample acquisition functions which tackle imbalance and
are compatible with IL constraints. We also consider IL as an imbalanced
learning problem instead of the established usage of knowledge distillation
against catastrophic forgetting. Here, imbalance effects are reduced during
inference through class prediction scaling. Evaluation is done with four visual
datasets and compares existing and proposed sample acquisition functions.
Results indicate that the proposed contributions have a positive effect and
reduce the gap between active and standard IL performance.

本论文研究了增量学习的问题，提出了一种采用主动和非均衡的采样方法，将增量学习视为一个非均衡学习问题，通过类别预测缩放降低不平衡的影响，针对四个视觉数据集进行了评估，并表明该方案对于减少主动和标准增量学习性能之间的差距产生了积极的效果。