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 1751–1800 of 6689 papers

TitleStatusHype
Improved Transformer for High-Resolution GANsCode1
Styleformer: Transformer based Generative Adversarial Networks with Style VectorCode1
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
Learning to See by Looking at NoiseCode1
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score EstimationCode1
Score-based Generative Modeling in Latent SpaceCode1
Densely connected normalizing flowsCode1
Data-Efficient Instance Generation from Instance DiscriminationCode1
The Image Local Autoregressive TransformerCode1
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 BenchmarkCode1
Semantic Palette: Guiding Scene Generation with Class ProportionsCode1
Multiresolution Equivariant Graph Variational AutoencoderCode1
GANs Can Play Lottery Tickets TooCode1
Controllable Person Image Synthesis with Spatially-Adaptive Warped NormalizationCode1
Gotta Go Fast When Generating Data with Score-Based ModelsCode1
NViSII: A Scriptable Tool for Photorealistic Image GenerationCode1
Efficient High-Resolution Image-to-Image Translation using Multi-Scale Gradient U-NetCode1
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual UnderstandingCode1
Scatterbrain: Unifying Sparse and Low-rank AttentionCode1
BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth ImageCode1
Improving Generation and Evaluation of Visual Stories via Semantic ConsistencyCode1
NeuroGen: activation optimized image synthesis for discovery neuroscienceCode1
PD-GAN: Probabilistic Diverse GAN for Image InpaintingCode1
EigenGAN: Layer-Wise Eigen-Learning for GANsCode1
Voice2Mesh: Cross-Modal 3D Face Model Generation from VoicesCode1
GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementCode1
Quaternion Generative Adversarial NetworksCode1
The Intrinsic Dimension of Images and Its Impact on LearningCode1
Towards Open-World Text-Guided Face Image Generation and ManipulationCode1
Image Super-Resolution via Iterative RefinementCode1
Spectrogram Inpainting for Interactive Generation of Instrument SoundsCode1
HoughNet: Integrating near and long-range evidence for visual detectionCode1
Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit EditingCode1
Aligning Latent and Image Spaces to Connect the UnconnectableCode1
Learning Semantic Person Image Generation by Region-Adaptive NormalizationCode1
Few-shot Image Generation via Cross-domain CorrespondenceCode1
MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image SynthesisCode1
Neural RGB-D Surface ReconstructionCode1
Handwriting TransformersCode1
InfinityGAN: Towards Infinite-Pixel Image SynthesisCode1
Regularizing Generative Adversarial Networks under Limited DataCode1
Content-Aware GAN CompressionCode1
Partition-Guided GANsCode1
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable DirectionsCode1
Text to Image Generation with Semantic-Spatial Aware GANCode1
Dual Contrastive Loss and Attention for GANsCode1
Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and TranslationCode1
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative ModelsCode1
Few-shot Semantic Image Synthesis Using StyleGAN PriorCode1
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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