Memory replay may be key to learning in biological brains, which manage to learn new tasks continually without catastrophically interfering with previous knowledge. On the other hand, artificial neural networks suffer from catastrophic forgetting and tend to only perform well on tasks that they were recently trained on. In this work we explore the application of latent space based memory replay for classification using artificial neural networks. We are able to preserve good performance in previous tasks by storing only a small percentage of the original data in a compressed latent space version.

翻译：记忆回放可能是生物大脑学习的关键,生物大脑能够持续学习新任务,而不会对先前的知识造成灾难性干扰。 另一方面,人工神经网络遭受灾难性的遗忘,而且往往只能很好地完成最近训练的任务。我们在此工作中探索了利用人造神经网络进行分类的潜在空间内存回放的应用。我们通过将原始数据中一小部分储存在压缩的潜伏空间版本中,能够保持以往任务的好成绩。