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Image Generation On Celeba 256X256
Image Generation On Celeba 256X256
평가 지표
bpd
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
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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