Density Estimation On Cifar 10

Metriken

NLL (bits/dim)

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
DDPM	3.69	Denoising Diffusion Probabilistic Models
MRCNF	3.54	Multi-Resolution Continuous Normalizing Flows
FFJORD	3.4	FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
RNODE	3.38	How to train your neural ODE: the world of Jacobian and kinetic regularization
Pixel CNN	3.03	Conditional Image Generation with PixelCNN Decoders
Flow matching	2.99	Flow Matching for Generative Modeling
score SDE	2.99	Score-Based Generative Modeling through Stochastic Differential Equations
Pixel CNN ++	2.92	PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
Image Transformer	2.90	Image Transformer
VDM	2.65	Variational Diffusion Models
BSI	2.64	Generative Modeling with Bayesian Sample Inference
MULAN	2.55	Diffusion Models With Learned Adaptive Noise
i-DODE	2.42	Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
MAF	-	Masked Autoregressive Flow for Density Estimation

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Density Estimation On Cifar 10 | SOTA | HyperAI