Heterogeneous Prototype Learning From Contaminated Faces Across Domains via Disentangling Latent Factors

Meng Pang, Binghui Wang, Mang Ye, Yiu Ming Cheung, Yintao Zhou, Wei Huang, Bihan Wen

Research output: Contribution to journalJournal articlepeer-review

3 Citations (Scopus)

Abstract

This article studies an emerging practical problem called heterogeneous prototype learning (HPL). Unlike the conventional heterogeneous face synthesis (HFS) problem that focuses on precisely translating a face image from a source domain to another target one without removing facial variations, HPL aims at learning the variation-free prototype of an image in the target domain while preserving the identity characteristics. HPL is a compounded problem involving two cross-coupled subproblems, that is, domain transfer and prototype learning (PL), thus making most of the existing HFS methods that simply transfer the domain style of images unsuitable for HPL. To tackle HPL, we advocate disentangling the prototype and domain factors in their respective latent feature spaces and then replacing the source domain with the target one for generating a new heterogeneous prototype. In doing so, the two subproblems in HPL can be solved jointly in a unified manner. Based on this, we propose a disentangled HPL framework, dubbed DisHPL, which is composed of one encoder–decoder generator and two discriminators. The generator and discriminators play adversarial games such that the generator embeds contaminated images into a prototype feature space only capturing identity information and a domain-specific feature space, while generating realistic-looking heterogeneous prototypes. Experiments on various heterogeneous datasets with diverse variations validate the superiority of DisHPL.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusE-pub ahead of print - 1 May 2024

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Disentangled representation learning (DRL)
  • Faces
  • generative adversarial learning
  • Generative adversarial networks
  • Generators
  • Hafnium
  • heterogeneous face synthesis (HFS)
  • heterogeneous prototype learning (HPL)
  • Image reconstruction
  • Prototypes
  • Representation learning

Fingerprint

Dive into the research topics of 'Heterogeneous Prototype Learning From Contaminated Faces Across Domains via Disentangling Latent Factors'. Together they form a unique fingerprint.

Cite this