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 4576–4600 of 6689 papers

TitleStatusHype
Improving GANs for Long-Tailed Data through Group Spectral RegularizationCode1
Vision-Language Matching for Text-to-Image Synthesis via Generative Adversarial Networks—0
Text to Image Generation: Leaving no Language Behind—0
T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-UpCode0
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning—0
Enhancing Diffusion-Based Image Synthesis with Robust Classifier GuidanceCode0
Out-of-distribution Detection via Frequency-regularized Generative ModelsCode1
Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork—0
Time flies by: Analyzing the Impact of Face Ageing on the Recognition Performance with Synthetic Data—0
Paint2Pix: Interactive Painting based Progressive Image Synthesis and EditingCode1
Understanding Attention for Vision-and-Language TasksCode0
StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3Code1
Riemannian Diffusion Models—0
Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion ModelCode1
SGM-Net: Semantic Guided Matting Net—0
Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI—0
Memory-Driven Text-to-Image Generation—0
Layout-Bridging Text-to-Image Synthesis—0
ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion DesignCode0
High-Frequency Space Diffusion Models for Accelerated MRICode1
Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning—0
Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and GenerationCode0
Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield NetworksCode1
Analog Bits: Generating Discrete Data using Diffusion Models with Self-ConditioningCode1
Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images—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