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Image Generation On Imagenet 32X32

Metrics

bpd

Results

Performance results of various models on this benchmark

Paper TitleRepository
Real NVP (Dinh et al., 2017)4.28Density estimation using Real NVP
Glow (Kingma and Dhariwal, 2018)4.09Glow: Generative Flow with Invertible 1x1 Convolutions
MintNet4.06MintNet: Building Invertible Neural Networks with Masked Convolutions
Residual Flow4.01Residual Flows for Invertible Generative Modeling
BIVA Maaloe et al. (2019)3.96BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
NVAE w/ flow3.92NVAE: A Deep Hierarchical Variational Autoencoder
ANF Huang et al. (2020)3.92Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
DDPM3.89Denoising Diffusion Probabilistic Models
PixelRNN3.86Pixel Recurrent Neural Networks
Flow++3.86Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
SPN Menick and Kalchbrenner (2019)3.85Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling-
DDPM++ (VP, NLL) + ST3.85Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
Gated PixelCNN3.83Conditional Image Generation with PixelCNN Decoders
Very Deep VAE3.8Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
δ-VAE3.77Preventing Posterior Collapse with delta-VAEs-
MRCNF3.77Multi-Resolution Continuous Normalizing Flows
Image Transformer3.77Image Transformer-
ScoreFlow3.76Maximum Likelihood Training of Score-Based Diffusion Models
Hourglass3.74Hierarchical Transformers Are More Efficient Language Models
Reflected Diffusion3.74Reflected Diffusion Models
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Image Generation On Imagenet 32X32 | SOTA | HyperAI