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Image Generation On Celeba 256X256

Metrics

bpd

Results

Performance results of various models on this benchmark

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

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