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 5451–5475 of 6689 papers

TitleStatusHype
A Generative Adversarial Network for AI-Aided Chair Design—0
SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills—0
Arbitrary Handwriting Image Style Transfer—0
Saliency Guided Optimization of Diffusion Latents—0
SALSA-TEXT : self attentive latent space based adversarial text generation—0
Using GANs to Synthesise Minimum Training Data for Deepfake Generation—0
SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis—0
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model—0
Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts—0
AGAN: Towards Automated Design of Generative Adversarial Networks—0
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
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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