  Generalized zero-shot learning recognizes inputs from both seen and unseen
classes. Yet, existing methods tend to be biased towards the classes seen
during training. In this paper, we strive to mitigate this bias. We propose a
bias-aware learner to map inputs to a semantic embedding space for generalized
zero-shot learning. During training, the model learns to regress to real-valued
class prototypes in the embedding space with temperature scaling, while a
margin-based bidirectional entropy term regularizes seen and unseen
probabilities. Relying on a real-valued semantic embedding space provides a
versatile approach, as the model can operate on different types of semantic
information for both seen and unseen classes. Experiments are carried out on
four benchmarks for generalized zero-shot learning and demonstrate the benefits
of the proposed bias-aware classifier, both as a stand-alone method or in
combination with generated features.
