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 5651–5700 of 6689 papers

TitleStatusHype
BlendScape: Enabling End-User Customization of Video-Conferencing Environments through Generative AI—0
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation—0
EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models—0
EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model—0
Emotion Classification of Children Expressions—0
Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering—0
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training—0
BlenderAlchemy: Editing 3D Graphics with Vision-Language Models—0
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack—0
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
End-to-End Vision Tokenizer Tuning—0
Energy-based Models for Earth Observation Applications—0
Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generators—0
Energy-Guided Optimization for Personalized Image Editing with Pretrained Text-to-Image Diffusion Models—0
EnerVerse-AC: Envisioning Embodied Environments with Action Condition—0
Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation—0
Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation—0
Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance—0
Enhanced Anime Image Generation Using USE-CMHSA-GAN—0
TactStyle: Generating Tactile Textures with Generative AI for Digital Fabrication—0
TAGE: Trustworthy Attribute Group Editing for Stable Few-shot Image Generation—0
Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks—0
Enhanced Multi-Scale Cross-Attention for Person Image Generation—0
Enhanced Sharp-GAN For Histopathology Image Synthesis—0
Enhanced Unsupervised Image-to-Image Translation Using Contrastive Learning and Histogram of Oriented Gradients—0
Enhance Image-to-Image Generation with LLaVA-generated Prompts—0
Enhancing AI Face Realism: Cost-Efficient Quality Improvement in Distilled Diffusion Models with a Fully Synthetic Dataset—0
Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds—0
Relaxed Wasserstein with Applications to GANs—0
Blended Latent Diffusion under Attention Control for Real-World Video Editing—0
Enhancing Diffusion Models for High-Quality Image Generation—0
Enhancing Diffusion Models with 3D Perspective Geometry Constraints—0
Enhancing digital core image resolution using optimal upscaling algorithm: with application to paired SEM images—0
Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach—0
Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques—0
Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization—0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy—0
Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models—0
Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques—0
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Enhancing Privacy in ControlNet and Stable Diffusion via Split Learning—0
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling—0
Visual Polarization Measurement Using Counterfactual Image Generation—0
Visual Programming for Text-to-Image Generation and Evaluation—0
Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity—0
Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation—0
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework—0
ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport—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