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

Recent GAN inversion models focus on preserving image-specific detailsthrough various methods, e.g., generator tuning or feature mixing. While thoseare helpful for preserving details compared to a naiive low-rate latentinversion, they still fail to maintain high-frequency features precisely. Inthis paper, we point out that the existing GAN inversion models have inherentlimitations in both structural and training aspects, which preclude thedelicate reconstruction of high-frequency features. Especially, we prove thatthe widely-used loss term in GAN inversion, i.e., L2, is biased to reconstructlow-frequency features mainly. To overcome this problem, we propose a novel GANinversion model, coined WaGI, which enables to handle high-frequency featuresexplicitly, by using a novel wavelet-based loss term and a newly proposedwavelet fusion scheme. To the best of our knowledge, WaGI is the first attemptto interpret GAN inversion in the frequency domain. We demonstrate that WaGIshows outstanding results on both inversion and editing, compared to theexisting state-of-the-art GAN inversion models. Especially, WaGI robustlypreserves high-frequency features of images even in the editing scenario. Wewill release our code with the pre-trained model after the review.

WaGI : Wavelet-based GAN Inversion for Preserving High-frequency Image Details