  We propose a novel loss for generative models, dubbed as GRaWD (Generative
Random Walk Deviation), to improve learning representations of unexplored
visual spaces. Quality learning representation of unseen classes (or styles) is
crucial to facilitate novel image generation and better generative
understanding of unseen visual classes (a.k.a. Zero-Shot Learning, ZSL). By
generating representations of unseen classes from their semantic descriptions,
such as attributes or text, Generative ZSL aims at identifying unseen
categories discriminatively from seen ones. We define GRaWD by constructing a
dynamic graph, including the seen class/style centers and generated samples in
the current mini-batch. Our loss starts a random walk probability from each
center through visual generations produced from hallucinated unseen classes. As
a deviation signal, we encourage the random walk to eventually land after t
steps in a feature representation that is hard to classify to any of the seen
classes. We show that our loss can improve unseen class representation quality
on four text-based ZSL benchmarks on CUB and NABirds datasets and three
attribute-based ZSL benchmarks on AWA2, SUN, and aPY datasets. We also study
our loss's ability to produce meaningful novel visual art generations on
WikiArt dataset. Our experiments and human studies show that our loss can
improve StyleGAN1 and StyleGAN2 generation quality, creating novel art that is
significantly more preferred. Code will be made available.
