  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
critical to facilitate novel image generation and better generative
understanding of unseen visual classes, i.e., zero-shot learning (ZSL). By
generating representations of unseen classes based on their semantic
descriptions, e.g., attributes or text, generative ZSL attempts to
differentiate unseen from seen categories. The proposed GRaWD loss is defined
by constructing a dynamic graph that includes the seen class/style centers and
generated samples in the current minibatch. Our loss initiates 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
difficult to classify as any of the seen classes. We demonstrate that the
proposed loss can improve unseen class representation quality inductively on
text-based ZSL benchmarks on CUB and NABirds datasets and attribute-based ZSL
benchmarks on AWA2, SUN, and aPY datasets. In addition, we investigate the
ability of the proposed loss to generate meaningful novel visual art on the
WikiArt dataset. The results of experiments and human evaluations demonstrate
that the proposed GRaWD loss can improve StyleGAN1 and StyleGAN2 generation
quality and create novel art that is significantly more preferable. Our code is
made publicly available at https://github.com/Vision-CAIR/GRaWD.
