Continual learning (CL) models are designed to learn new tasks arriving
sequentially without re-training the network. However, real-world ML
applications have very limited label information and these models suffer from
catastrophic forgetting. To address these issues, we propose an unsupervised CL
model with task experts called Unsupervised Task Expert Lifelong Learning
(U-TELL) to continually learn the data arriving in a sequence addressing
catastrophic forgetting. During training of U-TELL, we introduce a new expert
on arrival of a new task. Our proposed architecture has task experts, a
structured data generator and a task assigner. Each task expert is composed of
3 blocks; i) a variational autoencoder to capture the task distribution and
perform data abstraction, ii) a k-means clustering module, and iii) a structure
extractor to preserve latent task data signature. During testing, task assigner
selects a suitable expert to perform clustering. U-TELL does not store or
replay task samples, instead, we use generated structured samples to train the
task assigner. We compared U-TELL with five SOTA unsupervised CL methods.
U-TELL outperformed all baselines on seven benchmarks and one industry dataset
for various CL scenarios with a training time over 6 times faster than the best
performing baseline.

提出了一种名为 U-TELL 的无监督连续学习（Continual learning）模型，该模型使用任务专家，通过训练生成的结构化样本进行任务分配，以解决由于有限标签信息导致的灾难性遗忘问题。