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 4451–4500 of 6689 papers

TitleStatusHype
A Framework For Image Synthesis Using Supervised Contrastive Learning—0
Using LLMs as prompt modifier to avoid biases in AI image generators—0
SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP—0
SAR Image Synthesis with Diffusion Models—0
SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation—0
SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network—0
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis—0
Scaffolding Creativity: Integrating Generative AI Tools and Real-world Experiences in Business Education—0
A Framework and Dataset for Abstract Art Generation via CalligraphyGAN—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
Affordance Diffusion: Synthesizing Hand-Object Interactions—0
Affective Image Filter: Reflecting Emotions from Text to Images—0
Scalable Ranked Preference Optimization for Text-to-Image Generation—0
Scalable, Tokenization-Free Diffusion Model Architectures with Efficient Initial Convolution and Fixed-Size Reusable Structures for On-Device Image Generation—0
AffectGAN: Affect-Based Generative Art Driven by Semantics—0
Scaling Diffusion Mamba with Bidirectional SSMs for Efficient Image and Video Generation—0
AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection—0
AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production—0
Scaling Inference Time Compute for Diffusion Models—0
Scaling Laws For Diffusion Transformers—0
Adversarial Training For Sketch Retrieval—0
Using Physics Informed Generative Adversarial Networks to Model 3D porous media—0
Using Scene Graph Context to Improve Image Generation—0
Using Text-to-Image Generation for Architectural Design Ideation—0
Adversarial Text-to-Image Synthesis: A Review—0
Scene Aware Person Image Generation through Global Contextual Conditioning—0
SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation—0
SceneComposer: Any-Level Semantic Image Synthesis—0
Adversarial Text to Continuous Image Generation—0
SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis—0
Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation—0
Scene Graph to Image Generation with Contextualized Object Layout Refinement—0
Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs—0
Layout Agnostic Scene Text Image Synthesis with Diffusion Models—0
U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models—0
Scene Text Magnifier—0
Scene Text Synthesis for Efficient and Effective Deep Network Training—0
Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation—0
Science-T2I: Addressing Scientific Illusions in Image Synthesis—0
FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency—0
SciRE-Solver: Accelerating Diffusion Models Sampling by Score-integrand Solver with Recursive Difference—0
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation—0
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation—0
Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance—0
Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects—0
UU-Nets Connecting Discriminator and Generator for Image to Image Translation—0
XOGAN: One-to-Many Unsupervised Image-to-Image Translation—0
Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions—0
Score Distillation Sampling with Learned Manifold Corrective—0
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization—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