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

Showing 17011750 of 6689 papers

TitleStatusHype
Diffusion Normalizing FlowCode1
The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA dataCode1
DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality LearningCode1
Vector-quantized Image Modeling with Improved VQGANCode1
Exploring constraints on CycleGAN-based CBCT enhancement for adaptive radiotherapyCode1
CLIP-Forge: Towards Zero-Shot Text-to-Shape GenerationCode1
ClimateGAN: Raising Climate Change Awareness by Generating Images of FloodsCode1
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image ManipulationCode1
Generative Modeling with Optimal Transport MapsCode1
Meta Internal LearningCode1
Autoregressive Diffusion ModelsCode1
FooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptionsCode1
GenCo: Generative Co-training for Generative Adversarial Networks with Limited DataCode1
Fully Spiking Variational AutoencoderCode1
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese PoemsCode1
PIRenderer: Controllable Portrait Image Generation via Semantic Neural RenderingCode1
Complementary Feature Enhanced Network with Vision Transformer for Image DehazingCode1
Image Shape Manipulation from a Single Augmented Training SampleCode1
Instance-Conditioned GANCode1
Is Attention Better Than Matrix Decomposition?Code1
Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit DetectionCode1
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark DetectionCode1
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image SynthesisCode1
LLVIP: A Visible-infrared Paired Dataset for Low-light VisionCode1
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image SynthesisCode1
Unsupervised Image Generation with Infinite Generative Adversarial NetworksCode1
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image GenerationCode1
Contextual Convolutional Neural NetworksCode1
ILVR: Conditioning Method for Denoising Diffusion Probabilistic ModelsCode1
Sketch Your Own GANCode1
Toward Spatially Unbiased Generative ModelsCode1
Continuous Language Generative FlowCode1
3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical ImagesCode1
SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition ModelsCode1
Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head MotionCode1
ViTGAN: Training GANs with Vision TransformersCode1
Bi-level Feature Alignment for Versatile Image Translation and ManipulationCode1
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series ImputationCode1
DocSynth: A Layout Guided Approach for Controllable Document Image SynthesisCode1
Improving Text-to-Image Synthesis Using Contrastive LearningCode1
Variational Diffusion ModelsCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
ResViT: Residual vision transformers for multi-modal medical image synthesisCode1
SITTA: Single Image Texture Translation for Data AugmentationCode1
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image GenerationCode1
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural ScenesCode1
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
Adaptive Convolutions for Structure-Aware Style TransferCode1
Self-Supervised GANs with Label AugmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified