Image Generation On Cifar 10

FID

Results

Performance results of various models on this benchmark

		Paper Title
PresGAN	52.202	Prescribed Generative Adversarial Networks
RESFLOW	48.29	-
Residual Flow	46.37	Residual Flows for Invertible Generative Modeling
GLF+perceptual loss (ours)	44.6	Generative Latent Flow
ProdPoly no activation functions	40.45	Deep Polynomial Neural Networks
ACGAN	35.47	-
DenseFlow-74-10	34.90	Densely connected normalizing flows
NVAE w/ flow	32.53	NVAE: A Deep Hierarchical Variational Autoencoder
QSNGAN	31.966	Quaternion Generative Adversarial Networks
WGAN-GP	29.3	Improved Training of Wasserstein GANs
MSGAN	28.73	Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
FOGAN	27.4	First Order Generative Adversarial Networks
HingeGAN	27.12	Gradient penalty from a maximum margin perspective
RSGAN-GP	25.60	The relativistic discriminator: a key element missing from standard GAN
NCSN	25.32	Generative Modeling by Estimating Gradients of the Data Distribution
SN-SMMDGAN	25.0	On gradient regularizers for MMD GANs
WGAN-GP + TT Update Rule	24.8	GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
NCP-VAE	24.08	A Contrastive Learning Approach for Training Variational Autoencoder Priors
CLR-GAN	23.3	CLR-GAN: Improving GANs Stability and Quality via Consistent Latent Representation and Reconstruction
SN-GANs	21.7	Spectral Normalization for Generative Adversarial Networks

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