Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representation

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.