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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3201–3250 of 6689 papers

TitleStatusHype
Artworks Reimagined: Exploring Human-AI Co-Creation through Body Prompting—0
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis—0
CoCoNO: Attention Contrast-and-Complete for Initial Noise Optimization in Text-to-Image Synthesis—0
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce—0
Dynamic Neural Style Transfer for Artistic Image Generation using VGG19—0
COCO-GAN: Conditional Coordinate Generative Adversarial Network—0
ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models—0
DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability—0
Dynamic Dual-Output Diffusion Models—0
Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation—0
DynamicControl: Adaptive Condition Selection for Improved Text-to-Image Generation—0
CoC-GAN: Employing Context Cluster for Unveiling a New Pathway in Image Generation—0
Dynamically Grown Generative Adversarial Networks—0
DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation—0
Cobra: Efficient Line Art COlorization with BRoAder References—0
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations—0
CoBIT: A Contrastive Bi-directional Image-Text Generation Model—0
ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary—0
Adversarial Training For Sketch Retrieval—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
2D GANs Meet Unsupervised Single-view 3D Reconstruction—0
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training—0
Coarse-to-Fine Searching for Efficient Generative Adversarial Networks—0
DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion—0
DualVAE: Controlling Colours of Generated and Real Images—0
Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion—0
Dual Supervised Learning—0
Cluster-guided Image Synthesis with Unconditional Models—0
Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics—0
Adversarial Text-to-Image Synthesis: A Review—0
DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation—0
Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis—0
CloTH-VTON: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON—0
ArtiFade: Learning to Generate High-quality Subject from Blemished Images—0
Adversarial Text to Continuous Image Generation—0
Dual Contradistinctive Generative Autoencoder—0
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis—0
Dual Adversarial Inference for Text-to-Image Synthesis—0
DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation—0
DT2I: Dense Text-to-Image Generation from Region Descriptions—0
CLoRA: A Contrastive Approach to Compose Multiple LoRA Models—0
Articulate That Object Part (ATOP): 3D Part Articulation from Text and Motion Personalization—0
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision—0
DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation—0
DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving—0
CLIP Model for Images to Textual Prompts Based on Top-k Neighbors—0
ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining—0
Opt-In Art: Learning Art Styles Only from Few Examples—0
Show:102550
← PrevPage 65 of 134Next →
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