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 2251–2300 of 6689 papers

TitleStatusHype
On the Diversity of Realistic Image SynthesisCode0
On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad GenerationsCode0
On gradient regularizers for MMD GANsCode0
Challenges in Disentangling Independent Factors of VariationCode0
On GANs and GMMsCode0
On Learning 3D Face Morphable Model from In-the-wild ImagesCode0
On the Cultural Gap in Text-to-Image GenerationCode0
CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned NormalizationCode0
ArchiGuesser -- AI Art Architecture Educational GameCode0
Distribution Matching Losses Can Hallucinate Features in Medical Image TranslationCode0
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient InversionCode0
One-shot Generative Domain Adaptation in 3D GANsCode0
cGANs with Projection DiscriminatorCode0
Distorting Embedding Space for Safety: A Defense Mechanism for Adversarially Robust Diffusion ModelsCode0
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNetCode0
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion ModelsCode0
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize RangeCode0
Dist-GAN: An Improved GAN using Distance ConstraintsCode0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
Omni-Directional Image Generation from Single Snapshot ImageCode0
Disentangling representations of retinal images with generative modelsCode0
Disentangling Mean Embeddings for Better Diagnostics of Image GeneratorsCode0
Offline Evaluation of Set-Based Text-to-Image GenerationCode0
Object-driven Text-to-Image Synthesis via Adversarial TrainingCode0
Observation-Guided Diffusion Probabilistic ModelsCode0
Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep LearningCode0
Disentangled Person Image GenerationCode0
CFCPalsy: Facial Image Synthesis with Cross-Fusion Cycle Diffusion Model for Facial Paralysis IndividualsCode0
Adversarial Information FactorizationCode0
Discriminator Rejection SamplingCode0
A Prompt Log Analysis of Text-to-Image Generation SystemsCode0
Normalizing Flow-Based Metric for Image GenerationCode0
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the DiscriminatorCode0
Non-Adversarial Image Synthesis with Generative Latent Nearest NeighborsCode0
Nonlinear 3D Face Morphable ModelCode0
Normalized DiversificationCode0
Noise Diffusion for Enhancing Semantic Faithfulness in Text-to-Image SynthesisCode0
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural NetworksCode0
Noise Robust Generative Adversarial NetworksCode0
NiNformer: A Network in Network Transformer with Token Mixing Generated Gating FunctionCode0
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural NetworksCode0
Neural Voxel Renderer: Learning an Accurate and Controllable Rendering ToolCode0
Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creationCode0
No Modes left behind: Capturing the data distribution effectively using GANsCode0
Adversarial Feedback LoopCode0
Neural Photo Editing with Introspective Adversarial NetworksCode0
Neural Characteristic Function Learning for Conditional Image GenerationCode0
CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion ModelsCode0
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion ModellingCode0
A New Perspective on Stabilizing GANs training: Direct Adversarial TrainingCode0
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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