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 2801–2850 of 6689 papers

TitleStatusHype
Few-shot Image Generation with Diffusion ModelsCode0
A Unified Debiasing Approach for Vision-Language Models across Modalities and TasksCode0
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
Few-shot Image Generation via Masked DiscriminationCode0
A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity MetricsCode0
Learning Texture Manifolds with the Periodic Spatial GANCode0
Align Beyond Prompts: Evaluating World Knowledge Alignment in Text-to-Image GenerationCode0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus EstimationCode0
Controllable Text-to-Image GenerationCode0
Learning Graph Representation of Agent DiffusersCode0
Learning Hierarchical Semantic Image Manipulation through Structured RepresentationsCode0
Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical InsightsCode0
FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression ReactionCode0
Multivariate rank via entropic optimal transport: sample efficiency and generative modelingCode0
Learning Generative Models of Tissue Organization with Supervised GANsCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance RegularizationCode0
LEAD: Min-Max Optimization from a Physical PerspectiveCode0
Learn, Imagine and Create: Text-to-Image Generation from Prior KnowledgeCode0
Feature Alignment as a Generative ProcessCode0
Layton: Latent Consistency Tokenizer for 1024-pixel Image Reconstruction and Generation by 256 TokensCode0
LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object IntegrationCode0
FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANsCode0
Layered Rendering Diffusion Model for Controllable Zero-Shot Image SynthesisCode0
Lateralization MLP: A Simple Brain-inspired Architecture for DiffusionCode0
LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption GenerationCode0
LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep LearningCode0
Learning Disentangled Discrete RepresentationsCode0
fastWDM3D: Fast and Accurate 3D Healthy Tissue InpaintingCode0
Latent Space Factorisation and Manipulation via Matrix Subspace ProjectionCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Latent Space is Feature Space: Regularization Term for GANs Training on Limited DatasetCode0
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition TimeCode0
Latent Flow TransformerCode0
LatentKeypointGAN: Controlling Images via Latent KeypointsCode0
Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing ImagesCode0
Latent Feature and Attention Dual Erasure Attack against Multi-View Diffusion Models for 3D Assets ProtectionCode0
Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target GenerationCode0
Fast Diffusion ModelCode0
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image ReconstructionCode0
Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image SegmentationCode0
A Layer-Based Sequential Framework for Scene Generation with GANsCode0
Laplacian-Steered Neural Style TransferCode0
Fast Adaptive Meta-Learning for Few-Shot Image GenerationCode0
Language Guided Adversarial PurificationCode0
Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image GeneratorCode0
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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