2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDIVMSP-18.4
Paper Title FAST INVERSE MAPPING OF FACE GANS
Authors Nicky Bayat, Vahid Reza Khazaie, Yalda Mohsenzadeh, Western University, Canada
SessionIVMSP-18: Faces in Images & Videos
LocationGather.Town
Session Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. While many studies have explored various training configurations and architectures for GANs, the problem of inverting the generator of GANs has been inadequately investigated. We train a ResNet architecture to map given faces to latent vectors that can be used to generate faces nearly identical to the target. We use a perceptual loss to embed face details in the recovered latent vector while maintaining visual quality using a pixel loss. The vast majority of studies on latent vector recovery are very slow and perform well only on generated images. We argue that our method can be used to determine a fast mapping between real human faces and latent-space vectors that contain most of the important face style details. At last, we demonstrate the performance of our approach on both real and generated faces.