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Title: Few-shot Image Generation via Cross-domain Correspondence
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.
Authors:
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Award ID(s):
2150012
Publication Date:
NSF-PAR ID:
10320562
Journal Name:
Conference on Computer Vision and Pattern Recognition (CVPR)
Sponsoring Org:
National Science Foundation
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