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 2751–2775 of 6689 papers

TitleStatusHype
Learning Portrait Style RepresentationsCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
Coordinate-based Texture Inpainting for Pose-Guided Image GenerationCode0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Learning Graph Representation of Agent DiffusersCode0
Learning Hierarchical Semantic Image Manipulation through Structured RepresentationsCode0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial NetworkCode0
Fine-grained Forecasting Models Via Gaussian Process Blurring EffectCode0
Learning Generative Models of Tissue Organization with Supervised GANsCode0
Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical InsightsCode0
Fine-grained Cross-modal Fusion based Refinement for Text-to-Image SynthesisCode0
Fine-Grained Alignment and Noise Refinement for Compositional Text-to-Image GenerationCode0
Auto-Embedding Generative Adversarial Networks for High Resolution Image SynthesisCode0
Co-occurrence Based Texture SynthesisCode0
Learning Disentangled Representations via Mutual Information EstimationCode0
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasetsCode0
Learn, Imagine and Create: Text-to-Image Generation from Prior KnowledgeCode0
Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold NetworkCode0
Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold NetworkCode0
LEAD: Min-Max Optimization from a Physical PerspectiveCode0
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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