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 4801–4850 of 6689 papers

TitleStatusHype
Atmospheric Turbulence Correction via Variational Deep Diffusion—0
Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout—0
Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models—0
Learning to Evaluate the Artness of AI-generated Images—0
Robust Image Ordinal Regression with Controllable Image GenerationCode0
Multi-object Video Generation from Single Frame Layouts—0
Generative Steganography Diffusion—0
Towards Segment Anything Model (SAM) for Medical Image Segmentation: A SurveyCode0
The Role of Data Curation in Image CaptioningCode0
Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge ReviewCode0
Guided Image Synthesis via Initial Image Editing in Diffusion ModelCode0
High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model—0
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance RegularizationCode0
SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis—0
IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers—0
Single-View Height Estimation with Conditional Diffusion Probabilistic Models—0
Tensor Decomposition for Model Reduction in Neural Networks: A Review—0
Controllable Image Generation via Collage Representations—0
Ray Conditioning: Trading Photo-consistency for Photo-realism in Multi-view Image Generation—0
Training-Free Location-Aware Text-to-Image Synthesis—0
Exploring Compositional Visual Generation with Latent Classifier Guidance—0
Quantitative analysis of collagen remodeling in pancreatic lesions using computationally translated collagen images derived from brightfield microscopy images—0
GlyphDiffusion: Text Generation as Image Generation—0
Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation—0
Dehazing-NeRF: Neural Radiance Fields from Hazy Images—0
Persistently Trained, Diffusion-assisted Energy-based ModelsCode0
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase—0
Using Text-to-Image Generation for Architectural Design Ideation—0
Adaptive Consensus Optimization Method for GANsCode0
FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits—0
ReelFramer: Human-AI Co-Creation for News-to-Video Translation—0
Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models—0
Modulating human brain responses via optimal natural image selection and synthetic image generation—0
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation—0
Identity Encoder for Personalized Diffusion—0
Text-Conditional Contextualized Avatars For Zero-Shot Personalization—0
M2T: Masking Transformers Twice for Faster Decoding—0
What does CLIP know about a red circle? Visual prompt engineering for VLMs—0
Control3Diff: Learning Controllable 3D Diffusion Models from Single-view Images—0
ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis—0
Gradient-Free Textual Inversion—0
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI—0
Unified Multi-Modal Image Synthesis for Missing Modality Imputation—0
Improving Vision-and-Language Navigation by Generating Future-View Image Semantics—0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model—0
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion—0
Deep Generative Modeling with Backward Stochastic Differential EquationsCode0
InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning—0
Few-shot Semantic Image Synthesis with Class Affinity Transfer—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