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 5526–5550 of 6689 papers

TitleStatusHype
Cycle-Consistent Generative Rendering for 2D-3D Modality TranslationCode0
Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input MechanismsCode1
Survey2Survey: A deep learning generative model approach for cross-survey image mapping—0
(f,Γ)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics—0
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning—0
Using GANs to Synthesise Minimum Training Data for Deepfake Generation—0
Two-Stream Appearance Transfer Network for Person Image Generation—0
DeepCFL: Deep Contextual Features Learning from a Single Image—0
Text-to-Image Generation Grounded by Fine-Grained User AttentionCode1
Blind Motion Deblurring through SinGAN Architecture—0
DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation—0
Towards Disentangling Latent Space for Unsupervised Semantic Face EditingCode1
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute EditingCode1
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial NetworksCode0
Creating cloud-free satellite imagery from image time series with deep learning—0
ControlVAE: Tuning, Analytical Properties, and Performance AnalysisCode4
Enhanced Balancing GAN: Minority-class Image GenerationCode1
BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed ImagesCode0
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait EditingCode1
Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising—0
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis—0
Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks—0
LEAD: Min-Max Optimization from a Physical PerspectiveCode0
Autoregressive Score Matching—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