  Zero-shot learning (ZSL) has been shown to be a promising approach to
generalizing a model to categories unseen during training by leveraging class
attributes, but challenges still remain. Recently, methods using generative
models to combat bias towards classes seen during training have pushed the
state of the art of ZSL, but these generative models can be slow or
computationally expensive to train. Additionally, while many previous ZSL
methods assume a one-time adaptation to unseen classes, in reality, the world
is always changing, necessitating a constant adjustment for deployed models.
Models unprepared to handle a sequential stream of data are likely to
experience catastrophic forgetting. We propose a meta-continual zero-shot
learning (MCZSL) approach to address both these issues. In particular, by
pairing self-gating of attributes and scaled class normalization with
meta-learning based training, we are able to outperform state-of-the-art
results while being able to train our models substantially faster
($>100\times$) than expensive generative-based approaches. We demonstrate this
by performing experiments on five standard ZSL datasets (CUB, aPY, AWA1, AWA2
and SUN) in both generalized zero-shot learning and generalized continual
zero-shot learning settings.
