HyperAI초신경
홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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HyperAI초신경

Conditional Image Generation On Cifar 10

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Intra-FID

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이 벤치마크에서 각 모델의 성능 결과

모델 이름
FID
Intra-FID
Paper TitleRepository
ADC-GAN5.6640.45Conditional GANs with Auxiliary Discriminative Classifier
Projection Discriminator17.5-cGANs with Projection Discriminator
DLSM2.25-Denoising Likelihood Score Matching for Conditional Score-based Data Generation
FQ-GAN5.34-Feature Quantization Improves GAN Training
StyleGAN2-ADA2.42-Training Generative Adversarial Networks with Limited Data
MIX-MHingeGAN3.6-Lessons Learned from the Training of GANs on Artificial Datasets
Improved GAN--Improved Techniques for Training GANs
WGAN-GP--Improved Training of Wasserstein GANs
StyleGAN2 + DiffAugment + D2D-CE2.26-Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training
DCGAN--Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
SGAN--Stacked Generative Adversarial Networks
aw-BigGAN6.89-Adaptive Weighted Discriminator for Training Generative Adversarial Networks
ContraGAN10.30-ContraGAN: Contrastive Learning for Conditional Image Generation
aw-SN-GAN8.03-Adaptive Weighted Discriminator for Training Generative Adversarial Networks
EGAN-Ent-VI--Calibrating Energy-based Generative Adversarial Networks
CR-BigGAN11.67-Consistency Regularization for Generative Adversarial Networks-
SteinGAN--Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
Splitting GAN--Class-Splitting Generative Adversarial Networks
ProdPoly no activation functions36.77-Deep Polynomial Neural Networks
BigGAN14.73-Large Scale GAN Training for High Fidelity Natural Image Synthesis
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