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Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting.
Face photo-sketch synthesis and recognition
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Prototypical networks for few-shot learning
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Diversity-sensitive conditional generative adversarial networks
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
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The unreasonable effectiveness of deep features as a perceptual metric
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Few-shot unsupervised image-to-image translation
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Normalized diversification
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Few-shot image generation with elastic weight consolidation
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Freeze discriminator: A simple baseline for fine-tuning gans
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Reliable fidelity and diversity metrics for generative models
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NVAE: A deep hierarchical variational autoencoder
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Minegan: effective knowledge transfer from gans to target domains with few images
Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, and Joost van de Weijer · 2020
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Semi-supervised learning for few-shot image-to-image translation
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Differentiable augmentation for data-efficient gan training
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