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 4301–4350 of 6689 papers

TitleStatusHype
Learning Implicit Brain MRI Manifolds with Deep Learning—0
Learning Joint ID-Textual Representation for ID-Preserving Image Synthesis—0
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator—0
Learning Latent Space Hierarchical EBM Diffusion Models—0
Learning Latent Structural Causal Models—0
Towards Universal Dataset Distillation via Task-Driven Diffusion—0
Learning Mixtures of Gaussians Using Diffusion Models—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
Learning Neural Random Fields with Inclusive Auxiliary Generators—0
Learning Object Context for Novel-View Scene Layout Generation—0
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation—0
Towards Visual Text Design Transfer Across Languages—0
Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated Image Detection—0
Learning Priors for Adversarial Autoencoders—0
Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning—0
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation—0
WAYLA - Generating Images from Eye Movements—0
Learnings from Scaling Visual Tokenizers for Reconstruction and Generation—0
Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation—0
Learning Spatial Relationships between Samples of Patent Image Shapes—0
Variational Conditional GAN for Fine-grained Controllable Image Generation—0
Learning the Visualness of Text Using Large Vision-Language Models—0
Learning to Detect Fake Face Images in the Wild—0
TraceMark-LDM: Authenticatable Watermarking for Latent Diffusion Models via Binary-Guided Rearrangement—0
Learning to Evaluate the Artness of AI-generated Images—0
Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras—0
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts—0
Learning to Generate Images with Perceptual Similarity Metrics—0
Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution—0
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters—0
Learning To Memorize Feature Hallucination for One-Shot Image Generation—0
Tracking multiple moving objects in images using Markov Chain Monte Carlo—0
Tractable loss function and color image generation of multinary restricted Boltzmann machine—0
Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Models—0
Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data—0
Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs—0
Learning Universal Policies via Text-Guided Video Generation—0
Learning Unnormalized Statistical Models via Compositional Optimization—0
Learning Versatile 3D Shape Generation with Improved AR Models—0
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models—0
Learning What and Where to Draw—0
LEDiff: Latent Exposure Diffusion for HDR Generation—0
LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance—0
Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images—0
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations—0
Weakly Supervised Annotations for Multi-modal Greeting Cards Dataset—0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis—0
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding—0
Let's Verify and Reinforce Image Generation Step by Step—0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation—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