Few-shot Image Generation via Cross-domain Correspondence

• URL: http://arxiv.org/abs/2104.06820v1
• Date: Tue, 13 Apr 2021 17:59:35 GMT
• Title: Few-shot Image Generation via Cross-domain Correspondence
• Authors: Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang
• Abstract summary: Training generative models, such as GANs, on a target domain containing limited examples can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. To further reduce overfitting, we present an anchor-based strategy to encourage different levels of realism over different regions in the latent space.
• Score: 98.2263458153041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. We propose to preserve the relative similarities and differences between instances in the source via a novel cross-domain distance consistency loss. To further reduce overfitting, we present an anchor-based strategy to encourage different levels of realism over different regions in the latent space. With extensive results in both photorealistic and non-photorealistic domains, we demonstrate qualitatively and quantitatively that our few-shot model automatically discovers correspondences between source and target domains and generates more diverse and realistic images than previous methods.

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