SOTAVerified

Image Generation

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: StyleGAN )

Papers

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Showing 4276–4300 of 6689 papers

TitleStatusHype
Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks—0
Learning AND-OR Templates for Professional Photograph Parsing and Guidance—0
One-to-one Mapping for Unpaired Image-to-image Translation—0
Towards Understanding the Generative Capability of Adversarially Robust Classifiers—0
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition—0
Learning Compositional Visual Concepts with Mutual Consistency—0
Control3Diff: Learning Controllable 3D Diffusion Models from Single-view Images—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Learning Detailed Radiance Manifolds for High-Fidelity and 3D-Consistent Portrait Synthesis from Monocular Image—0
Private Gradient Estimation is Useful for Generative Modeling—0
Learning Diffusion Texture Priors for Image Restoration—0
Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation—0
Towards Understanding the Mechanisms of Classifier-Free Guidance—0
Learning Disentangled Representations with Reference-Based Variational Autoencoders—0
Learning Dynamic Style Kernels for Artistic Style Transfer—0
Diffusion Models for Accurate Channel Distribution Generation—0
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling—0
Learning Energy-based Model via Dual-MCMC Teaching—0
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality—0
Wavelets Are All You Need for Autoregressive Image Generation—0
A Simple Background Augmentation Method for Object Detection with Diffusion Model—0
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning—0
Learning Generative Models with Goal-conditioned Reinforcement Learning—0
Learning geometry-image representation for 3D point cloud generation—0
A Simple Approach to Unifying Diffusion-based Conditional Generation—0
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified