English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a novel loss for generative models, dubbed as GRaWD (GenerativeRandom Walk Deviation), to improve learning representations of unexploredvisual spaces. Quality learning representation of unseen classes (or styles) iscritical to facilitate novel image generation and better generativeunderstanding of unseen visual classes, i.e., zero-shot learning (ZSL). Bygenerating representations of unseen classes based on their semanticdescriptions, e.g., attributes or text, generative ZSL attempts todifferentiate unseen from seen categories. The proposed GRaWD loss is definedby constructing a dynamic graph that includes the seen class/style centers andgenerated samples in the current minibatch. Our loss initiates a random walkprobability from each center through visual generations produced fromhallucinated unseen classes. As a deviation signal, we encourage the randomwalk to eventually land after t steps in a feature representation that isdifficult to classify as any of the seen classes. We demonstrate that theproposed loss can improve unseen class representation quality inductively ontext-based ZSL benchmarks on CUB and NABirds datasets and attribute-based ZSLbenchmarks on AWA2, SUN, and aPY datasets. In addition, we investigate theability of the proposed loss to generate meaningful novel visual art on theWikiArt dataset. The results of experiments and human evaluations demonstratethat the proposed GRaWD loss can improve StyleGAN1 and StyleGAN2 generationquality and create novel art that is significantly more preferable. Our code ismade publicly available at https://github.com/Vision-CAIR/GRaWD.

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