Abstract
An ability to grasp new concepts from their descriptions is one of the key features of human intelligence, and zero-shot learning (ZSL) aims to incorporate this property into machine learning models. In this paper, we theoretically investigate two very popular tricks used in ZSL: "normalize+scale" trick and attributes normalization and show how they help to preserve a signal's variance in a typical model during a forward pass. Next, we demonstrate that these two tricks are not enough to normalize a deep ZSL network. We derive a new initialization scheme, which allows us to demonstrate strong state-of-the-art results on 4 out of 5 commonly used ZSL datasets: SUN, CUB, AwA1, and AwA2 while being on average 2 orders faster than the closest runner-up. Finally, we generalize ZSL to a broader problem -- Continual Zero-Shot Learning (CZSL) and test our ideas in this new setup. The source code to reproduce all the results is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2006.11328
