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Image Generation On Celeba 256X256

Metrics

bpd

Results

Performance results of various models on this benchmark

		Paper Title	Repository
Glow (Kingma and Dhariwal, 2018)	1.03	Glow: Generative Flow with Invertible 1x1 Convolutions
Residual Flow	0.992	Residual Flows for Invertible Generative Modeling
MaCow (Unf)	0.95	MaCow: Masked Convolutional Generative Flow
Locally Masked PixelCNN	0.74	Locally Masked Convolution for Autoregressive Models
ANF Huang et al. (2020)	0.72	Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
LSGM	0.70	Score-based Generative Modeling in Latent Space
NVAE w/ flow	0.70	NVAE: A Deep Hierarchical Variational Autoencoder
MaCow (Var)	0.67	MaCow: Masked Convolutional Generative Flow
SPN Menick and Kalchbrenner (2019)	0.61	Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling	-
Efficient-VDVAE	0.51	Efficient-VDVAE: Less is more
StyleSwin	-	StyleSwin: Transformer-based GAN for High-resolution Image Generation
NCP-VAE	-	A Contrastive Learning Approach for Training Variational Autoencoder Priors	-
HiT-B	-	Improved Transformer for High-Resolution GANs
StyleALAE	-	Adversarial Latent Autoencoders
GLF+perceptual loss (ours)	-	Generative Latent Flow
MSP	-	Latent Space Factorisation and Manipulation via Matrix Subspace Projection
VQGAN	-	Taming Transformers for High-Resolution Image Synthesis

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Image Generation On Celeba 256X256 | SOTA | HyperAI