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 6301–6325 of 6689 papers

TitleStatusHype
Coordinate-based Texture Inpainting for Pose-Guided Image GenerationCode0
Class-Distinct and Class-Mutual Image Generation with GANsCode0
Self-Supervised GANs via Auxiliary Rotation LossCode0
Spatially Controllable Image Synthesis with Internal Representation CollagingCode0
IGNOR: Image-guided Neural Object Rendering—0
GAN Dissection: Visualizing and Understanding Generative Adversarial NetworksCode0
PCGAN: Partition-Controlled Human Image GenerationCode0
Generate, Segment and Refine: Towards Generic Manipulation SegmentationCode0
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic InstructionCode0
Adversarial Feedback LoopCode0
Attributing Fake Images to GANs: Learning and Analyzing GAN FingerprintsCode0
Synthetic Lung Nodule 3D Image Generation Using AutoencodersCode0
SEIGAN: Towards Compositional Image Generation by Simultaneously Learning to Segment, Enhance, and Inpaint—0
Entropy-regularized Optimal Transport Generative Models—0
Style and Content Disentanglement in Generative Adversarial Networks—0
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks—0
Towards Adversarial Denoising of Radar Micro-Doppler Signatures—0
A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions—0
An Interpretable Generative Model for Handwritten Digit Image Synthesis—0
Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation—0
An Infinite Parade of Giraffes: Expressive Augmentation and Complexity Layers for Cartoon DrawingCode0
Disentangling Latent Factors of Variational Auto-Encoder with Whitening—0
Student's t-Generative Adversarial Networks—0
Fast Face Image Synthesis with Minimal Training—0
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks—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