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 4451–4475 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
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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