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 2876–2900 of 6689 papers

TitleStatusHype
DeepFace: Face Generation using Deep Learning—0
Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales—0
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion—0
Image Synthesis via Semantic Composition—0
GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs—0
Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields—0
GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis—0
DeepFake Detection by Analyzing Convolutional Traces—0
Grounding Text-To-Image Diffusion Models For Controlled High-Quality Image Generation—0
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks—0
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds—0
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image—0
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation—0
Deep Domain-Adversarial Image Generation for Domain Generalisation—0
Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Models—0
Guided Co-Modulated GAN for 360° Field of View Extrapolation—0
Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers—0
Guided Flows for Generative Modeling and Decision Making—0
Guided Frequency Loss for Image Restoration—0
Gradpaint: Gradient-Guided Inpainting with Diffusion Models—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces—0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising—0
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks—0
Bias in Large Language Models Across Clinical Applications: A Systematic 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