10-shot image generation
5 papers with code • 1 benchmarks • 1 datasets
CloudSEN12
Most implemented papers
Training Generative Adversarial Networks with Limited Data
NVlabs/stylegan2-ada
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NeurIPS 2020
We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5. 59 to 2. 42.
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
Generative adversarial networks (GANs) have shown outstanding performance on a wide range of problems in computer vision, graphics, and machine learning, but often require numerous training data and heavy computational resources.
Few-shot Image Generation via Cross-domain Correspondence
utkarshojha/few-shot-gan-adaptation
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CVPR 2021
Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.
Few-shot Image Generation via Adaptation-Aware Kernel Modulation
yunqing-me/AdAM
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However, a major limitation of existing methods is that their knowledge preserving criteria consider only source domain/source task, and they fail to consider target domain/adaptation task in selecting source model's knowledge, casting doubt on their suitability for setups of different proximity between source and target domain.
Transferring GANs: generating images from limited data
WuChenshen/MeRGAN
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ECCV 2018
Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models.
