Image Generation On Imagenet 32X32

bpd

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	bpd
nvae-a-deep-hierarchical-variational	3.92
glow-generative-flow-with-invertible-1x1	4.09
mintnet-building-invertible-neural-networks	4.06
residual-flows-for-invertible-generative	4.01
variational-diffusion-models	3.72
generating-high-fidelity-images-with-subscale	3.85
stylegan-xl-scaling-stylegan-to-large-diverse	-
preventing-posterior-collapse-with-delta-vaes	3.77
pagoda-progressive-growing-of-a-one-step	-
very-deep-vaes-generalize-autoregressive-1	3.8
pixel-recurrent-neural-networks	3.86
hierarchical-transformers-are-more-efficient	3.74
score-matching-model-for-unbounded-data-score-1	3.85
improved-techniques-for-maximum-likelihood	3.43
multi-resolution-continuous-normalizing-flows	3.77
flow-improving-flow-based-generative-models	3.86
biva-a-very-deep-hierarchy-of-latent	3.96
reflected-diffusion-models	3.74
neural-diffusion-models	3.55
denoising-diffusion-probabilistic-models	3.89
self-supervised-gan-analysis-and-improvement-1	-
on-maximum-likelihood-training-of-score-based	3.76
input-perturbation-reduces-exposure-bias-in	-
generative-modeling-with-bayesian-sample	3.44
image-transformer	3.77
flow-matching-for-generative-modeling	3.53
quasi-conservative-score-based-generative	-
augmented-normalizing-flows-bridging-the-gap	3.92
conditional-image-generation-with-pixelcnn	3.83
the-gan-is-dead-long-live-the-gan-a-modern	-
densely-connected-normalizing-flows	3.63
density-estimation-using-real-nvp	4.28
neural-flow-diffusion-models-learnable	3.34