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

Showing 65516575 of 6689 papers

TitleStatusHype
Self-supervised monocular depth estimation from oblique UAV videosCode0
Design a Delicious Lunchbox in StyleCode0
Self-Supervised Object Detection via Generative Image SynthesisCode0
High-Resolution Deep Convolutional Generative Adversarial NetworksCode0
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR DataCode0
LumiPath -- Towards Real-time Physically-based Rendering on Embedded DevicesCode0
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN ImagesCode0
AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI SynthesisCode0
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information FlowCode0
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual ExplanationsCode0
DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual DesignCode0
High-Resolution Network for Photorealistic Style TransferCode0
High-resolution Rainy Image Synthesis: Learning from RenderingCode0
M3Dsynth: A dataset of medical 3D images with AI-generated local manipulationsCode0
Self-Supervised Ultrasound to MRI Fetal Brain Image SynthesisCode0
DeSIGN: Design Inspiration from Generative NetworksCode0
Open-Source Acceleration of Stable-Diffusion.cpp Deployable on All DevicesCode0
Understanding Visual Concepts Across ModelsCode0
Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry GenerationCode0
Synthesizing Images of Humans in Unseen PosesCode0
MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic AlignmentCode0
Calibrating Energy-based Generative Adversarial NetworksCode0
Semantically Decomposing the Latent Spaces of Generative Adversarial NetworksCode0
RecTable: Fast Modeling Tabular Data with Rectified FlowCode0
Histopathology DatasetGAN: Synthesizing Large-Resolution Histopathology DatasetsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified