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 5801–5825 of 6689 papers

TitleStatusHype
Coarse-to-Fine Gaze Redirection with Numerical and Pictorial GuidanceCode0
Text-Guided Neural Image InpaintingCode1
Semantic Image Manipulation Using Scene GraphsCode1
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood EstimationCode1
Evolving Normalization-Activation LayersCode1
Rethinking Spatially-Adaptive Normalization—0
GANSpace: Discovering Interpretable GAN ControlsCode2
Feature Quantization Improves GAN TrainingCode1
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the DiscriminatorCode0
Structural-analogy from a Single Image PairCode1
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders—0
Open Domain Dialogue Generation with Latent Images—0
A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing—0
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic DataCode1
Object-Centric Image Generation with Factored Depths, Locations, and Appearances—0
Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs—0
Edge Guided GANs with Contrastive Learning for Semantic Image SynthesisCode1
Learning Generative Models of Tissue Organization with Supervised GANsCode0
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation—0
Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images—0
Semantically Multi-modal Image SynthesisCode1
Learning Implicit Surface Light FieldsCode2
Controllable Person Image Synthesis with Attribute-Decomposed GANCode1
Cycle Text-To-Image GAN with BERTCode1
StrokeCoder: Path-Based Image Generation from Single Examples using Transformers—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