Most of the existing artificial neural networks(ANNs) fail to learn
continually due to catastrophic forgetting, while humans can do the same by
maintaining previous tasks' performances. Although storing all the previous
data can alleviate the problem, it takes a large memory, infeasible in
real-world utilization. We propose a continual zero-shot learning model(A-CZSL)
that is more suitable in real-case scenarios to address the issue that can
learn sequentially and distinguish classes the model has not seen during
training. Further, to enhance the reliability, we develop A-CZSL for a single
head continual learning setting where task identity is revealed during the
training process but not during the testing. We present a hybrid network that
consists of a shared VAE module to hold information of all tasks and
task-specific private VAE modules for each task. The model's size grows with
each task to prevent catastrophic forgetting of task-specific skills, and it
includes a replay approach to preserve shared skills. We demonstrate our hybrid
model outperforms the baselines and is effective on several datasets, i.e.,
CUB, AWA1, AWA2, and aPY. We show our method is superior in class sequentially
learning with ZSL(Zero-Shot Learning) and GZSL(Generalized Zero-Shot Learning).

文章提出了一种基于 VAE 模块的连续零样本学习模型 (A-CZSL)，用于解决人工神经网络与人类区别在于传统模型很难取得不同阶段任务良好的表现，模型大小在任务逐步增加的同时增长以防遗忘，并展示其在零样本学习和广义零样本学习上的优越性能。