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 3851–3875 of 6689 papers

TitleStatusHype
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation—0
An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks—0
Understanding Attention for Vision-and-Language Tasks—0
One-Shot Generalization in Deep Generative Models—0
Understanding Diffusion Models: A Unified Perspective—0
Understanding Diffusion Models by Feynman's Path Integral—0
One-step Diffusion Models with f-Divergence Distribution Matching—0
An Impartial Transformer for Story Visualization—0
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation—0
One-Way Ticket: Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models—0
On Fairness of Unified Multimodal Large Language Model for Image Generation—0
Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation—0
On Geometrical Properties of Text Token Embeddings for Strong Semantic Binding in Text-to-Image Generation—0
On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models—0
Understanding Subjectivity through the Lens of Motivational Context in Model-Generated Image Satisfaction—0
Only-Style: Stylistic Consistency in Image Generation without Content Leakage—0
Understanding the Limitations of Diffusion Concept Algebra Through Food—0
Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks—0
On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance—0
On Synthetic Texture Datasets: Challenges, Creation, and Curation—0
On the Adversarial Robustness of Generative Autoencoders in the Latent Space—0
On the Design of Diffusion-based Neural Speech Codecs—0
On The Distribution of Penultimate Activations of Classification Networks—0
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP—0
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer—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