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 4751–4800 of 6689 papers

TitleStatusHype
Chop & Learn: Recognizing and Generating Object-State Compositions—0
CIMGEN: Controlled Image Manipulation by Finetuning Pretrained Generative Models on Limited Data—0
CipherDM: Secure Three-Party Inference for Diffusion Model Sampling—0
Speed up the inference of diffusion models via shortcut MCMC sampling—0
Circuit Complexity Bounds for Visual Autoregressive Model—0
Clarifying MCMC-based training of modern EBMs : Contrastive Divergence versus Maximum Likelihood—0
Classification Diffusion Models: Revitalizing Density Ratio Estimation—0
Classification under strategic adversary manipulation using pessimistic bilevel optimisation—0
Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms—0
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning—0
CG-NeRF: Conditional Generative Neural Radiance Fields—0
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation—0
Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy—0
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs—0
CLIPAG: Towards Generator-Free Text-to-Image Generation—0
SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model—0
Spherical Dense Text-to-Image Synthesis—0
ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining—0
CLIP Model for Images to Textual Prompts Based on Top-k Neighbors—0
Spherical Image Generation from a Single Normal Field of View Image by Considering Scene Symmetry—0
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis—0
CLoRA: A Contrastive Approach to Compose Multiple LoRA Models—0
CloTH-VTON: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON—0
Adoption of Watermarking Measures for AI-Generated Content and Implications under the EU AI Act—0
Cluster-guided Image Synthesis with Unconditional Models—0
Coarse-to-Fine Searching for Efficient Generative Adversarial Networks—0
CoBIT: A Contrastive Bi-directional Image-Text Generation Model—0
Cobra: Efficient Line Art COlorization with BRoAder References—0
CoC-GAN: Employing Context Cluster for Unveiling a New Pathway in Image Generation—0
Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation—0
COCO-GAN: Conditional Coordinate Generative Adversarial Network—0
CoCoNO: Attention Contrast-and-Complete for Initial Noise Optimization in Text-to-Image Synthesis—0
Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer—0
Codebook Transfer with Part-of-Speech for Vector-Quantized Image Modeling—0
CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation—0
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation—0
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images—0
CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks—0
cFineGAN: Unsupervised multi-conditional fine-grained image generation—0
Coherent Zero-Shot Visual Instruction Generation—0
COIN: Counterfactual Image Generation for VQA Interpretation—0
Video Generation with Learned Action Prior—0
COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design—0
Collaborative Control for Geometry-Conditioned PBR Image Generation—0
CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs—0
Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
Collage Diffusion—0
Collaging Class-specific GANs for Semantic Image Synthesis—0
Collapse Resistant Deep Convolutional GAN for Multi-Object Image Generation—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