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 5751–5775 of 6689 papers

TitleStatusHype
Example-Guided Image Synthesis using Masked Spatial-Channel Attention and Self-Supervision—0
Example-Guided Scene Image Synthesis using Masked Spatial-Channel Attention and Patch-Based Self-Supervision—0
Example-Guided Style-Consistent Image Synthesis From Semantic Labeling—0
TCFG: Tangential Damping Classifier-free Guidance—0
Exemplar-based Generative Facial Editing—0
Exocentric to Egocentric Image Generation via Parallel Generative Adversarial Network—0
Experiments on Generative AI-Powered Parametric Modeling and BIM for Architectural Design—0
Explainable Concept Generation through Vision-Language Preference Learning—0
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation—0
TCIG: Two-Stage Controlled Image Generation with Quality Enhancement through Diffusion—0
Explicit Gradient Learning—0
Explicit Gradient Learning for Black-Box Optimization—0
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
Exploiting Relationship for Complex-scene Image Generation—0
Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage—0
Explore the Power of Synthetic Data on Few-shot Object Detection—0
Explore the vulnerability of black-box models via diffusion models—0
Exploring Cellular Protein Localization Through Semantic Image Synthesis—0
Exploring Compositional Visual Generation with Latent Classifier Guidance—0
Visual Relationship Detection using Scene Graphs: A Survey—0
TDRI: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generation—0
Exploring Disentangled and Controllable Human Image Synthesis: From End-to-End to Stage-by-Stage—0
VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL—0
Teaching Metric Distance to Autoregressive Multimodal Foundational Models—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