  We investigate learning feature-to-feature translator networks by alternating
back-propagation as a general-purpose solution to zero-shot learning (ZSL)
problems. It is a generative model-based ZSL framework. In contrast to models
based on generative adversarial networks (GAN) or variational autoencoders
(VAE) that require auxiliary networks to assist the training, our model
consists of a single conditional generator that maps class-level semantic
features and Gaussian white noise vector accounting for instance-level latent
factors to visual features, and is trained by maximum likelihood estimation.
The training process is a simple yet effective alternating back-propagation
process that iterates the following two steps: (i) the inferential
back-propagation to infer the latent factors of each observed example, and (ii)
the learning back-propagation to update the model parameters. We show that,
with slight modifications, our model is capable of learning from incomplete
visual features for ZSL. We conduct extensive comparisons with existing
generative ZSL methods on five benchmarks, demonstrating the superiority of our
method in not only ZSL performance but also convergence speed and computational
cost. Specifically, our model outperforms the existing state-of-the-art methods
by a remarkable margin up to 3.1% and 4.0% in ZSL and generalized ZSL settings,
respectively.
