Human Latent Metrics: Perceptual and Cognitive Response Correlates to Distance in GAN Latent Space for Facial Images

Kye Shimizu, Naoto Ienaga, Kazuma Takada, Maki Sugimoto, Shunichi Kasahara

研究成果: Conference contribution

抄録

Generative adversarial networks (GANs) generate high-dimensional vector spaces (latent spaces) that can interchangeably represent vectors as images. Advancements have extended their ability to computationally generate images indistinguishable from real images such as faces, and more importantly, to manipulate images using their inherit vector values in the latent space. This interchangeability of latent vectors has the potential to calculate not only the distance in the latent space, but also the human perceptual and cognitive distance toward images, that is, how humans perceive and recognize images. However, it is still unclear how the distance in the latent space correlates with human perception and cognition. Our studies investigated the relationship between latent vectors and human perception or cognition through psycho-visual experiments that manipulates the latent vectors of face images. In the perception study, a change perception task was used to examine whether participants could perceive visual changes in face images before and after moving an arbitrary distance in the latent space. In the cognition study, a face recognition task was utilized to examine whether participants could recognize a face as the same, even after moving an arbitrary distance in the latent space. Our experiments show that the distance between face images in the latent space correlates with human perception and cognition for visual changes in face imagery, which can be modeled with a logistic function. By utilizing our methodology, it will be possible to interchangeably convert between the distance in the latent space and the metric of human perception and cognition, potentially leading to image processing that better reflects human perception and cognition.

本文言語English
ホスト出版物のタイトルProceedings - SAP 2022
ホスト出版物のサブタイトルACM Symposium on Applied Perception
編集者Stephen N. Spencer
出版社Association for Computing Machinery, Inc
ISBN(電子版)9781450394550
DOI
出版ステータスPublished - 2022 9月 22
イベント19th ACM Symposium on Applied Perception, SAP 2022 - Virtual, Online, United States
継続期間: 2022 9月 222022 9月 23

出版物シリーズ

名前Proceedings - SAP 2022: ACM Symposium on Applied Perception

Conference

Conference19th ACM Symposium on Applied Perception, SAP 2022
国/地域United States
CityVirtual, Online
Period22/9/2222/9/23

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • 計算理論と計算数学
  • ソフトウェア
  • 応用数学

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