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 this https URL based on the avalanche
framework to reproduce our results and encourage future research.

在线持续学习在不断变化的数据流中直接学习，并存储来自该流中的最少量数据，本文通过对国内外文献中解决在线持续学习问题的方法进行实证评估，重点关注图像分类中的增量类别设置，通过比较在 Split-CIFAR100 和 Split-TinyImagenet 数据集上的平均准确率、遗忘性、稳定性和表示质量的指标，发现大多数方法都存在稳定性和欠拟合问题，但所学习的表示与相同计算预算下的独立同分布训练相当。同时发现基本的经验回放方法，经过适当调整和实施，是一种非常强大的基准方法。我们在 avalanche 框架上发布了我们的模块化和可扩展的代码库来复现我们的结果，并鼓励未来的研究。

在线持续学习的综合实证评估

A Comprehensive Empirical Evaluation on Online Continual Learning

Humans are capable of learning new tasks without forgetting previous ones,
while neural networks fail due to catastrophic forgetting between new and
previously-learned tasks. We consider a class-incremental setting which means
that the task-ID is unknown at inference time. The imbalance between old and
new classes typically results in a bias of the network towards the newest ones.
This imbalance problem can either be addressed by storing exemplars from
previous tasks, or by using image replay methods. However, the latter can only
be applied to toy datasets since image generation for complex datasets is a
hard problem.
We propose a solution to the imbalance problem based on generative feature
replay which does not require any exemplars. To do this, we split the network
into two parts: a feature extractor and a classifier. To prevent forgetting, we
combine generative feature replay in the classifier with feature distillation
in the feature extractor. Through feature generation, our method reduces the
complexity of generative replay and prevents the imbalance problem. Our
approach is computationally efficient and scalable to large datasets.
Experiments confirm that our approach achieves state-of-the-art results on
CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed
for exemplar-based continual learning. Code available at
https://github.com/xialeiliu/GFR-IL.

对于神经网络中的灾难性遗忘问题和老新类别不均衡问题，本文提出了一种基于生成式特征回放和特征蒸馏的解决方案，可以不使用明显的旧任务样本，在 CIFAR-100 和 ImageNet 等数据集上实现了最先进的结果，存储空间成本低。