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 5926–5950 of 6689 papers

TitleStatusHype
Pixel-Level Domain TransferCode0
Towards Bridging the Performance Gaps of Joint Energy-based ModelsCode0
Feature Alignment as a Generative ProcessCode0
PixelNN: Example-based Image SynthesisCode0
Towards Conceptual CompressionCode0
Modeling Emotions and Ethics with Large Language ModelsCode0
-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite DimensionsCode0
Test-Time Scaling of Diffusion Models via Noise Trajectory SearchCode0
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image SynthesisCode0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
Backdooring Bias into Text-to-Image ModelsCode0
Ink removal from histopathology whole slide images by combining classification, detection and image generation modelsCode0
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion ModelsCode0
CS^2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human InterventionCode0
EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density ModelingCode0
Wasserstein Generative Learning of Conditional DistributionCode0
FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression ReactionCode0
Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion ModelsCode0
Conditional Image Generation with PixelCNN DecodersCode0
Mode Seeking Generative Adversarial Networks for Diverse Image SynthesisCode0
RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and StylesCode0
Discovery of Single Independent Latent VariableCode0
Modular Generative Adversarial NetworksCode0
Modular StoryGAN with Background and Theme Awareness for Story VisualizationCode0
RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image SynthesisCode0
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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