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

Showing 56765700 of 6689 papers

TitleStatusHype
Controllable Image Synthesis via SegVAE0
Modeling Artistic Workflows for Image Generation and EditingCode1
Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature EqualizationsCode1
Lessons Learned from the Training of GANs on Artificial DatasetsCode1
Closed-Form Factorization of Latent Semantics in GANsCode2
WormPose: Image synthesis and convolutional networks for pose estimation in C. elegansCode1
Impression Space from Deep Template Network0
InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive LearningCode1
NVAE: A Deep Hierarchical Variational AutoencoderCode1
Benefiting Deep Latent Variable Models via Learning the Prior and Removing Latent Regularization0
SofGAN: A Portrait Image Generator with Dynamic StylingCode1
Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks0
Gradient Origin NetworksCode1
Meta-Learning Divergences of Variational Inference0
Partially Conditioned Generative Adversarial Networks0
HoughNet: Integrating near and long-range evidence for bottom-up object detectionCode1
GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisCode1
BézierSketch: A generative model for scalable vector sketchesCode1
Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On0
PerceptionGAN: Real-world Image Construction from Provided Text through Perceptual Understanding0
Image Shape Manipulation from a Single Augmented Training SampleCode1
Sliced Iterative Normalizing FlowsCode1
Deep Geometric Texture SynthesisCode1
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition0
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved GeneralizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified