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Few-shot Image Generation via Cross-domain Correspondence
[article]
2021
arXiv
pre-print
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
arXiv:2104.06820v1
fatcat:wydnviay6favlbw5yi3d2dkzka