  Zero-shot learning (ZSL) is a promising approach to generalizing a model to
categories unseen during training by leveraging class attributes, but
challenges remain. Recently, methods using generative models to combat bias
towards classes seen during training have pushed state of the art, but these
generative models can be slow or computationally expensive to train. Also,
these generative models assume that the attribute vector of each unseen class
is available a priori at training, which is not always practical. 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
of deployed models. Models unprepared to handle a sequential stream of data are
likely to experience catastrophic forgetting. We propose a Meta-learned
Attribute self-Interaction Network (MAIN) for continual ZSL. By pairing
attribute self-interaction trained using meta-learning with inverse
regularization of the attribute encoder, we are able to outperform
state-of-the-art results without leveraging the unseen class attributes while
also being able to train our models substantially faster (>100x) than expensive
generative-based approaches. We demonstrate this with experiments on five
standard ZSL datasets (CUB, aPY, AWA1, AWA2, and SUN) in the generalized
zero-shot learning and continual (fixed/dynamic) zero-shot learning settings.
Extensive ablations and analyses demonstrate the efficacy of various components
proposed.
