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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5101–5125 of 6689 papers

TitleStatusHype
Generative Probabilistic Image Colorization—0
Weakly Supervised Keypoint Discovery—0
NeurInt-Learning Interpolation by Neural ODEs—0
Fully Spiking Variational AutoencoderCode1
Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial NetworkCode0
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese PoemsCode1
LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders—0
ComicGAN: Text-to-Comic Generative Adversarial Network—0
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging—0
PIRenderer: Controllable Portrait Image Generation via Semantic Neural RenderingCode1
SketchHairSalon: Deep Sketch-based Hair Image Synthesis—0
Complementary Feature Enhanced Network with Vision Transformer for Image DehazingCode1
Image Synthesis via Semantic Composition—0
The State of the Art when using GPUs in Devising Image Generation Methods Using Deep Learning—0
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation—0
Image Shape Manipulation from a Single Augmented Training SampleCode1
Conditional MoCoGAN for Zero-Shot Video Generation—0
Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN—0
MSGDD-cGAN: Multi-Scale Gradients Dual Discriminator Conditional Generative Adversarial Network—0
Per Garment Capture and Synthesis for Real-time Virtual Try-on—0
Instance-Conditioned GANCode1
Is Attention Better Than Matrix Decomposition?Code1
Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks—0
MRI Reconstruction Using Deep Energy-Based ModelCode0
LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption GenerationCode0
Show:102550
← PrevPage 205 of 268Next →
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