CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning

• URL: http://arxiv.org/abs/2203.08612v2
• Date: Fri, 8 Mar 2024 08:02:59 GMT
• Title: CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning
• Authors: Yue Wang, Ran Yi, Luying Li, Ying Tai, Chengjie Wang, Lizhuang Ma
• Abstract summary: CtlGAN is a new few-shot artistic portraits generation model with a novel contrastive transfer learning strategy. We adapt a pretrained StyleGAN in the source domain to a target artistic domain with no more than 10 artistic faces. We propose a new encoder which embeds real faces into Z+ space and proposes a dual-path training strategy to better cope with the adapted decoder.
• Score: 77.27821665339492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating artistic portraits is a challenging problem in computer vision. Existing portrait stylization models that generate good quality results are based on Image-to-Image Translation and require abundant data from both source and target domains. However, without enough data, these methods would result in overfitting. In this work, we propose CtlGAN, a new few-shot artistic portraits generation model with a novel contrastive transfer learning strategy. We adapt a pretrained StyleGAN in the source domain to a target artistic domain with no more than 10 artistic faces. To reduce overfitting to the few training examples, we introduce a novel Cross-Domain Triplet loss which explicitly encourages the target instances generated from different latent codes to be distinguishable. We propose a new encoder which embeds real faces into Z+ space and proposes a dual-path training strategy to better cope with the adapted decoder and eliminate the artifacts. Extensive qualitative, quantitative comparisons and a user study show our method significantly outperforms state-of-the-arts under 10-shot and 1-shot settings and generates high quality artistic portraits. The code will be made publicly available.

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