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 6176–6200 of 6689 papers

TitleStatusHype
Generative models for visualising abstract social processes: Guiding streetview image synthesis of StyleGAN2 with indices of deprivation—0
Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens—0
Generative networks as inverse problems with fractional wavelet scattering networks—0
A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methods—0
A Closer Look at Few-shot Image Generation—0
Generative OpenMax for Multi-Class Open Set Classification—0
Backbone Augmented Training for Adaptations—0
Generative Portrait Shadow Removal—0
Generative Probabilistic Image Colorization—0
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models—0
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis—0
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition—0
Generative Steganographic Flow—0
Generative Steganography Diffusion—0
Generative Steganography Network—0
Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis—0
Generative Zero-Shot Composed Image Retrieval—0
Generative Zero-shot Network Quantization—0
Generative Zoo—0
Generator Born from Classifier—0
Generator Matching: Generative modeling with arbitrary Markov processes—0
Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks—0
Generic Camera Attribute Control using Bayesian Optimization—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)—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