Image Generation On Cifar 10

Métriques

FID

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	FID
lessons-learned-from-the-training-of-gans-on	8.17
Modèle 2	35.47
eagan-efficient-two-stage-evolutionary	10.14
deep-polynomial-neural-networks	40.45
adversarial-score-identity-distillation	1.396
regularizing-generative-adversarial-networks	8.46
refining-generative-process-with	1.77
gans-trained-by-a-two-time-scale-update-rule	24.8
generative-modeling-with-explicit-memory	1.22
scire-solver-efficient-sampling-of-diffusion	1.76
Modèle 11	48.29
clr-gan-improving-gans-stability-and-quality	23.3
the-relativistic-discriminator-a-key-element	25.60
subspace-diffusion-generative-models	2.17
block-flow-learning-straight-flow-on-data	2.29
adversarially-slicing-generative-networks	1.36
robust-diffusion-gan-using-semi-unbalanced	2.95
eagan-efficient-two-stage-evolutionary	9.91
prescribed-generative-adversarial-networks	52.202
peergan-generative-adversarial-networks-with	21.55
densely-connected-normalizing-flows	34.90
discriminator-contrastive-divergence-semi	16.24
generative-latent-flow-a-framework-for-non	44.6
poisson-flow-generative-models	2.48
score-based-generative-modeling-in-latent	6.89
quaternion-generative-adversarial-networks	31.966
score-identity-distillation-exponentially	1.71
improving-gan-training-with-probability-ratio	10.7
likelihood-training-of-schrodinger-bridge-1	3.01
deep-polynomial-neural-networks	16.79
blackout-diffusion-generative-diffusion	4.58
connections-between-support-vector-machines	27.12
generative-modeling-by-estimating-gradients	25.32
lt-gan-self-supervised-gan-with-latent	9.80
the-disappearance-of-timestep-embedding-in	3.074
Modèle 36	17.87
ncp-vae-variational-autoencoders-with-noise-1	24.08
compensation-sampling-for-improved	1.50
diffusion-models-are-innate-one-step	1.54
elucidating-the-exposure-bias-in-diffusion	1.8
contextual-convolutional-neural-networks	19.66
pfgm-unlocking-the-potential-of-physics	1.74
mode-seeking-generative-adversarial-networks	28.73
improved-techniques-for-training-score-based	10.87
the-gan-is-dead-long-live-the-gan-a-modern	1.96
dual-contradistinctive-generative-autoencoder-1	17.9
on-gradient-regularizers-for-mmd-gans	25.0
dist-gan-an-improved-gan-using-distance	17.61
consistency-models	5.83
spectral-normalization-for-generative	21.7
progressive-distillation-for-fast-sampling-of-1	3.00
posterior-mean-matching-generative-modeling	2.18
truncated-consistency-models	2.05
variational-schrodinger-diffusion-models	2.28
stable-rank-normalization-for-improved	19.83
progressive-distillation-for-fast-sampling-of-1	2.57
learning-stationary-markov-processes-with	18.27
discriminator-contrastive-divergence-semi	21.67
denoising-diffusion-probabilistic-models	3.17
improved-training-of-wasserstein-gans	29.3
flow-matching-for-generative-modeling	6.35
genie-higher-order-denoising-diffusion	5.97
residual-flows-for-invertible-generative	46.37
vitgan-training-gans-with-vision-transformers	6.66
differentiable-augmentation-for-data	4.61
first-order-generative-adversarial-networks	27.4
consistency-models-made-easy	2.11
-nets-deep-polynomial-neural-networks	16.79
nvae-a-deep-hierarchical-variational	32.53
direct-discriminative-optimization-your-1	1.30