# Active Class Incremental Learning for Imbalanced Datasets

arXiv: https://arxiv.org/abs/2008.10968 · HTML: https://mlsanity.com/paper/2008.10968/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Online Continual Learning for Embedded Devices](https://mlsanity.com/paper/2203.10681/) (2022)
- [Gradient Reweighting: Towards Imbalanced Class-Incremental Learning](https://mlsanity.com/paper/2402.18528/) (2024)
- [Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task](https://mlsanity.com/paper/2405.17779/) (2024)
- [Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation](https://mlsanity.com/paper/2412.06470/) (2024)
- [Can I see an Example? Active Learning the Long Tail of Attributes and Relations](https://mlsanity.com/paper/2203.06215/) (2022)
- [Incremental Learning with Repetition via Pseudo-Feature Projection](https://mlsanity.com/paper/2502.19922/) (2025)
- [ACIL: Active Class Incremental Learning for Image Classification](https://mlsanity.com/paper/2602.04252/) (2026)
- [EndoCIL: A Class-Incremental Learning Framework for Endoscopic Image Classification](https://mlsanity.com/paper/2510.17200/) (2025)

## Abstract (arXiv)

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.