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。