  Focusing on discriminative zero-shot learning, in this work we introduce a
novel mechanism that dynamically augments during training the set of seen
classes to produce additional fictitious classes. These fictitious classes
diminish the model's tendency to fixate during training on attribute
correlations that appear in the training set but will not appear in newly
exposed classes. The proposed model is tested within the two formulations of
the zero-shot learning framework; namely, generalized zero-shot learning (GZSL)
and classical zero-shot learning (CZSL). Our model improves the
state-of-the-art performance on the CUB dataset and reaches comparable results
on the other common datasets, AWA2 and SUN. We investigate the strengths and
weaknesses of our method, including the effects of catastrophic forgetting when
training an end-to-end zero-shot model.
