Abstract

Unsupervised continual learning (learning representations without any knowledge about task identity)

Introduction

挖坑写法，however, most of these techniques have focused on a sequence of tasks in which both the identity of the task (task label) and boundaries between tasks are provided; moreover, they often focus on the supervised learning setting, where class labels for each data point are given.
Unsupervised, 1) the absence of task labels (or indeed well-defined tasks themselves) 2) the absence of external supervision such as class labels, regression targets, or external rewards.

Method

用了生成模型来刻画数据分布

Conclusion

The proposed approach, performs task inference via a mixture-of-Gaussians latent space, and uses dynamic expansion and mixture generative replay to instantiate new concepts and minimize catastrophic forgetting.

Key points: 代码开源，paper with code stars多；no knowledge of task labels and boundaries; 实验数据集比较简单，MNIST和Ominiglot；文章写的一般；