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 5226–5250 of 6689 papers

TitleStatusHype
Multi-Resolution Continuous Normalizing FlowsCode0
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction—0
Flow Guided Transformable Bottleneck Networks for Motion Retargeting—0
Improved Transformer for High-Resolution GANsCode1
CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis—0
Styleformer: Transformer based Generative Adversarial Networks with Style VectorCode1
Inverting Adversarially Robust Networks for Image SynthesisCode0
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANsCode0
Ultrasound Classification of Breast Masses Using a Comprehensive Nakagami Imaging and Machine Learning Framework—0
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
Toward Accurate and Realistic Outfits Visualization with Attention to Details—0
Generative Adversarial Networks in finance: an overview—0
Score-based Generative Modeling in Latent SpaceCode1
AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection—0
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis—0
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score EstimationCode1
Learning to See by Looking at NoiseCode1
A multi-stage GAN for multi-organ chest X-ray image generation and segmentation—0
Densely connected normalizing flowsCode1
Data-Efficient Instance Generation from Instance DiscriminationCode1
Neural Monge Map estimation and its applicationsCode0
Generative Flows with Invertible Attentions—0
Efficient training for future video generation based on hierarchical disentangled representation of latent variables—0
High Resolution Solar Image Generation using Generative Adversarial Networks—0
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
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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