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 6476–6500 of 6689 papers

TitleStatusHype
Adversarial Audio SynthesisCode1
Combinets: Creativity via Recombination of Neural Networks—0
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial NetworksCode0
No Modes left behind: Capturing the data distribution effectively using GANsCode0
Improved Training of Generative Adversarial Networks Using Representative Features—0
Deep Interactive EvolutionCode0
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation—0
Decoupled Learning for Conditional Adversarial NetworksCode0
Composite Functional Gradient Learning of Generative Adversarial ModelsCode0
Semi-supervised FusedGAN for Conditional Image Generation—0
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis—0
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network—0
SketchyGAN: Towards Diverse and Realistic Sketch to Image SynthesisCode0
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition—0
Learning Implicit Brain MRI Manifolds with Deep Learning—0
Feature Map Variational Auto-Encoders—0
MaskGAN: Better Text Generation via Filling in the _______—0
Deformable GANs for Pose-based Human Image GenerationCode0
PixelSNAIL: An Improved Autoregressive Generative ModelCode1
On Using Backpropagation for Speech Texture Generation and Voice Conversion—0
On the Diversity of Realistic Image SynthesisCode0
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground TruthCode0
Integral Equations and Machine Learning—0
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition—0
Unsupervised Histopathology Image Synthesis—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