  Despite the advancement of supervised image recognition algorithms, their
dependence on the availability of labeled data and the rapid expansion of image
categories raise the significant challenge of zero-shot learning. Zero-shot
learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled
classes to reduce human labeling effort. In this paper, we propose a novel
progressive ensemble network model with multiple projected label embeddings to
address zero-shot image recognition. The ensemble network is built by learning
multiple image classification functions with a shared feature extraction
network but different label embedding representations, which enhance the
diversity of the classifiers and facilitate information transfer to unlabeled
classes. A progressive training framework is then deployed to gradually label
the most confident images in each unlabeled class with predicted pseudo-labels
and update the ensemble network with the training data augmented by the
pseudo-labels. The proposed model performs training on both labeled and
unlabeled data. It can naturally bridge the domain shift problem in visual
appearances and be extended to the generalized zero-shot learning scenario. We
conduct experiments on multiple ZSL datasets and the empirical results
demonstrate the efficacy of the proposed model.
