  Zero-shot learning (ZSL) aims at understanding unseen categories with no
training examples from class-level descriptions. To improve the discriminative
power of zero-shot learning, we model the visual learning process of unseen
categories with inspiration from the psychology of human creativity for
producing novel art. We relate ZSL to human creativity by observing that
zero-shot learning is about recognizing the unseen and creativity is about
creating a likable unseen. We introduce a learning signal inspired by
creativity literature that explores the unseen space with hallucinated
class-descriptions and encourages careful deviation of their visual feature
generations from seen classes while allowing knowledge transfer from seen to
unseen classes. Empirically, we show consistent improvement over the state of
the art of several percents on the largest available benchmarks on the
challenging task or generalized ZSL from a noisy text that we focus on, using
the CUB and NABirds datasets. We also show the advantage of our approach on
Attribute-based ZSL on three additional datasets (AwA2, aPY, and SUN). Code is
available.
