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 2551–2600 of 6689 papers

TitleStatusHype
Adversarial Code Learning for Image Generation—0
Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later—0
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
Can segmentation models be trained with fully synthetically generated data?—0
Temporal Evolution of Knee Osteoarthritis: A Diffusion-based Morphing Model for X-ray Medical Image Synthesis—0
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging—0
Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation—0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb Prosthetic Users—0
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models—0
Diffusion Models as Data Mining Tools—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP—0
Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines—0
Can Artificial Intelligence Reconstruct Ancient Mosaics?—0
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks—0
Diffusion Instruction Tuning—0
Causal Adversarial Network for Learning Conditional and Interventional Distributions—0
Diffusion idea exploration for art generation—0
Adversarial Audio Super-Resolution with Unsupervised Feature Losses—0
Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Models—0
Hand1000: Generating Realistic Hands from Text with Only 1,000 Images—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields—0
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection—0
Anysize GAN: A solution to the image-warping problem—0
Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Human-Like Sketches—0
Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images—0
Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance—0
CompAlign: Improving Compositional Text-to-Image Generation with a Complex Benchmark and Fine-Grained Feedback—0
DiffUMI: Training-Free Universal Model Inversion via Unconditional Diffusion for Face Recognition—0
Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery—0
CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer—0
AnyScene: Customized Image Synthesis with Composited Foreground—0
Dilated Spatial Generative Adversarial Networks for Ergodic Image Generation—0
CAGAN: Text-To-Image Generation with Combined Attention GANs—0
CAFLOW: Conditional Autoregressive Flows—0
Adversarial Attacks on Image Generation With Made-Up Words—0
HairNeRF: Geometry-Aware Image Synthesis for Hairstyle Transfer—0
Halluci-Net: Scene Completion by Exploiting Object Co-occurrence Relationships—0
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances—0
DiffusionBlocks: Blockwise Training for Generative Models via Score-Based Diffusion—0
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling—0
Diffusion Beats Autoregressive: An Evaluation of Compositional Generation in Text-to-Image Models—0
Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler—0
AnyLogo: Symbiotic Subject-Driven Diffusion System with Gemini Status—0
CacheQuant: Comprehensively Accelerated Diffusion Models—0
Cache Me if You Can: Accelerating Diffusion Models through Block Caching—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