  Zero-shot learning (ZSL) aims at understanding unseen categories with no
training examples from class-level descriptions. To improve the discriminative
power of ZSL, we model the visual learning process of unseen categories with
inspiration from the psychology of human creativity for producing novel art.
First, we propose CIZSL-v1 as a creativity inspired model for generative ZSL.
We relate ZSL to human creativity by observing that ZSL 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. Second, CIZSL-v2 is proposed as an
improved version of CIZSL-v1 for generative zero-shot learning. CIZSL-v2
consists of an investigation of additional inductive losses for unseen classes
along with a semantic guided discriminator. Empirically, we show consistently
that CIZSL losses can improve generative ZSL models on the challenging task of
generalized ZSL from a noisy text on CUB and NABirds datasets. We also show the
advantage of our approach to Attribute-based ZSL on AwA2, aPY, and SUN
datasets. We also show that CIZSL-v2 has improved performance compared to
CIZSL-v1.
