# On Class Orderings for Incremental Learning

arXiv: https://arxiv.org/abs/2007.02145 · HTML: https://mlsanity.com/paper/2007.02145/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [On the importance of cross-task features for class-incremental learning](https://mlsanity.com/paper/2106.11930/) (2021)
- [Optimizing Class Distribution in Memory for Multi-Label Online Continual Learning](https://mlsanity.com/paper/2209.11469/) (2022)
- [Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks](https://mlsanity.com/paper/2203.05692/) (2022)
- [Cooperative data-driven modeling](https://mlsanity.com/paper/2211.12971/) (2022)
- [Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2401.18054/) (2024)
- [Continual learning for surface defect segmentation by subnetwork creation and selection](https://mlsanity.com/paper/2312.05100/) (2023)
- [Beyond Classification: Dynamic Adapter Routing for Continual Multimodal Retrieval](https://mlsanity.com/paper/2605.31229/) (2026)
- [Utilizing Priming to Identify Optimal Class Ordering to Alleviate Catastrophic Forgetting](https://mlsanity.com/paper/2212.12643/) (2022)
- [Adapt & Align: Continual Learning with Generative Models Latent Space Alignment](https://mlsanity.com/paper/2312.13699/) (2023)

## Abstract (arXiv)

The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incrementally learned classifiers. We propose a method to compute various orderings for a dataset. The orderings are derived by simulated annealing optimization from the confusion matrix and reflect different incremental learning scenarios, including maximally and minimally confusing tasks. We evaluate a wide range of state-of-the-art incremental learning methods on the proposed orderings. Results show that orderings can have a significant impact on performance and the ranking of the methods.