在线持续学习的综合实证评估
A Comprehensive Empirical Evaluation on Online Continual Learning
AI总结：
本文在类别增量图像分类基准上系统比较多种在线持续学习方法，发现多数方法存在稳定性与欠拟合问题，但表征质量可媲美同计算预算下的 i.i.d. 训练，且调优后的经验回放是强基线。
摘要：
在线持续学习旨在通过直接在分布随时间变化的数据流上学习，并仅存储来自该数据流的极少量数据，来更贴近真实的在线学习体验。在本次实证评估中，我们评估了文献中应对在线持续学习的多种方法。更具体地说，我们聚焦于图像分类背景下的类别增量设置，其中学习器必须从数据流中增量学习新类别。我们在 Split-CIFAR100 和 Split-TinyImagenet 基准上比较这些方法，并测量它们的平均准确率、遗忘程度、稳定性以及表征质量，从而不仅在训练结束时，也在整个训练期间评估算法的各个方面。我们发现，大多数方法都存在稳定性和欠拟合问题。然而，在相同计算预算下，所学得的表征与独立同分布（i.i.d.）训练所得表征相当。结果中没有出现明显的优胜者，而经过适当调参和正确实现的基础经验回放是一个非常强的基线。我们基于 avalanche 框架发布了模块化、可扩展的代码库：https://github.com/AlbinSou/ocl_survey，以复现我们的结果并鼓励未来研究。
英文摘要：
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at https://github.com/AlbinSou/ocl_survey based on the avalanche framework to reproduce our results and encourage future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
