Image Generation On Imagenet 32X32

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bpd

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	bpd	Paper Title	Repository
NVAE w/ flow	3.92	NVAE: A Deep Hierarchical Variational Autoencoder	-
Glow (Kingma and Dhariwal, 2018)	4.09	Glow: Generative Flow with Invertible 1x1 Convolutions	-
MintNet	4.06	MintNet: Building Invertible Neural Networks with Masked Convolutions	-
Residual Flow	4.01	Residual Flows for Invertible Generative Modeling	-
VDM	3.72	Variational Diffusion Models	-
SPN Menick and Kalchbrenner (2019)	3.85	Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling	-
StyleGAN-XL	-	StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets	-
δ-VAE	3.77	Preventing Posterior Collapse with delta-VAEs	-
PaGoDA	-	PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher	-
Very Deep VAE	3.8	Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images	-
PixelRNN	3.86	Pixel Recurrent Neural Networks	-
Hourglass	3.74	Hierarchical Transformers Are More Efficient Language Models	-
DDPM++ (VP, NLL) + ST	3.85	Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation	-
i-DODE	3.43	Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs	-
MRCNF	3.77	Multi-Resolution Continuous Normalizing Flows	-
Flow++	3.86	Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design	-
BIVA Maaloe et al. (2019)	3.96	BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling	-
Reflected Diffusion	3.74	Reflected Diffusion Models	-
NDM	3.55	Neural Diffusion Models	-
DDPM	3.89	Denoising Diffusion Probabilistic Models	-

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