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 4951–4975 of 6689 papers

TitleStatusHype
Segmentation-Reconstruction-Guided Facial Image De-occlusion—0
Score-Based Generative Modeling with Critically-Damped Langevin DiffusionCode1
More Control for Free! Image Synthesis with Semantic Diffusion Guidance—0
A Shared Representation for Photorealistic Driving SimulatorsCode1
Self-Supervised Image-to-Text and Text-to-Image SynthesisCode0
Multimodal Conditional Image Synthesis with Product-of-Experts GANs—0
Assessing a Single Image in Reference-Guided Image Synthesis—0
A Generic Approach for Enhancing GANs by Regularized Latent Optimization—0
CG-NeRF: Conditional Generative Neural Radiance Fields—0
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization—0
DoodleFormer: Creative Sketch Drawing with Transformers—0
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and EditingCode1
Adaptive Feature Interpolation for Low-Shot Image GenerationCode1
MoFaNeRF: Morphable Facial Neural Radiance FieldCode1
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation—0
Panoptic-aware Image-to-Image Translation—0
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space OptimizationCode1
TISE: Bag of Metrics for Text-to-Image Synthesis EvaluationCode1
3D-Aware Semantic-Guided Generative Model for Human SynthesisCode1
GANSeg: Learning to Segment by Unsupervised Hierarchical Image GenerationCode1
Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning—0
Exploration into Translation-Equivariant Image QuantizationCode0
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
SegDiff: Image Segmentation with Diffusion Probabilistic ModelsCode1
FDA-GAN: Flow-based Dual Attention GAN for Human Pose Transfer—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