Training generative adversarial networks (GANs) with limited  data generally results in deteriorated performance and collapsed models. To conquer this challenge, we are inspired by the latest observation of Kalibhat et al. (2020); Chen et al.(2021d) , that one can discover independently trainable and highly sparse subnetworks (a.k.a., lottery tickets) from GANs. Treating this as an inductive prior, we decompose the data-hungry GAN traininginto two sequential sub-problems: (i) identifying the lottery ticket from the original GAN ; then (ii) training the found sparse subnetwork with aggressive data and feature augmentations. Both sub-problems re-use the same small training setof real images. Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet). Besides, our training framework also displays powerful few-shot generalization ability, i.e., generating high-fidelity images by training from scratch with just 100 real images, without any pre-training. Codes are available at: URL
Training generative adversarial networks (GANs) with limited real image data generally results in deteriorated performance and collapsed models. To conquer this challenge, we are inspired by the latest observations , that one can discover independently trainable and highly sparse subnetworks (a.k.a., lottery tickets) from GANs. Treating this as an inductive prior, we suggest a brand-new angle towards data-efficient GAN training: by first identifying the lottery ticket from the original GAN using the small training set of real images; and then focusing on training that sparse subnetwork by re-using the same set. Both steps have lower complexity and are more data-efficient to train. We find our coordinated framework to offer orthogonal gains to existing real image data augmentation methods, and we additionally offer a new feature-level augmentation that can be applied together with them . Comprehensive experiments endorse the effectiveness of our proposed framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN-V2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet). Our training framework also displays powerful few-shot generalization ability, i.e., generating high-fidelity images by training from scratch with just 100 real images, without any pre-training. Codes are available at: URL
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real image
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observation of Kalibhat et al. (2020); Chen et al.(2021d)
observations
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decompose the data-hungry GAN traininginto two sequential sub-problems: (i)
suggest a brand-new angle towards data-efficient GAN training: by first
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; then (ii) training the found sparse subnetwork with aggressive data and feature augmentations. Both sub-problems re-use the same small training setof real images. Such a coordinated framework enables us to focus on lower-complexity and
using the small training set of real images; and then focusing on training that sparse subnetwork by re-using the same set. Both steps have lower complexity and are
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sub-problems, effectively stabilizing training and improving convergence
to train. We find our coordinated framework to offer orthogonal gains to existing real image data augmentation methods, and we additionally offer a new feature-level augmentation that can be applied together with them
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ultra-data-efficient training
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StyleGAN2
StyleGAN-V2
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Besides, our
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