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 6051–6100 of 6689 papers

TitleStatusHype
Semantic Object Accuracy for Generative Text-to-Image SynthesisCode0
Small-GAN: Speeding Up GAN Training Using Core-sets—0
Fair Generative Modeling via Weak SupervisionCode0
Consistency Regularization for Generative Adversarial Networks—0
Study of Deep Generative Models for Inorganic Chemical CompositionsCode0
TRB: A Novel Triplet Representation for Understanding 2D Human BodyCode0
Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis—0
A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis—0
Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)Code0
Attention Guided Metal Artifact Correction in MRI using Deep Neural Networks—0
Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation—0
Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages—0
BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach—0
Gradient penalty from a maximum margin perspectiveCode0
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image SynthesisCode0
Implicit competitive regularization in GANsCode0
Image Generation and Recognition (Emotions)—0
Visual Indeterminacy in GAN Art—0
Defending Neural Backdoors via Generative Distribution ModelingCode0
Text-to-Image Synthesis Based on Machine Generated Captions—0
A Conditional Generative Model for Predicting Material Microstructures from Processing Methods—0
Bootstrapping Conditional GANs for Video Game Level Generation—0
FiNet: Compatible and Diverse Fashion Image Inpainting—0
VTNFP: An Image-Based Virtual Try-On Network With Body and Clothing Feature PreservationCode0
ClothFlow: A Flow-Based Model for Clothed Person GenerationCode0
FaceForensics++: Learning to Detect Manipulated Facial ImagesCode0
FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On—0
Instance-Guided Context Rendering for Cross-Domain Person Re-Identification—0
Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos—0
Semantics-Enhanced Adversarial Nets for Text-to-Image Synthesis—0
Unsupervised Generative 3D Shape Learning from Natural Images—0
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision—0
Understanding and Stabilizing GANs' Training Dynamics with Control TheoryCode0
RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image SynthesisCode0
RPGAN: random paths as a latent space for GAN interpretabilityCode0
Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-quality Data for Training—0
Is There Mode Collapse? A Case Study on Face Generation and Its Black-box Calibration—0
Stochastic Conditional Generative Networks with Basis Decomposition—0
BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS—0
mustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesis—0
Towards Controllable and Interpretable Face Completion via Structure-Aware and Frequency-Oriented Attentive GANs—0
Relative Pixel Prediction For Autoregressive Image Generation—0
Conditional Invertible Neural Networks for Guided Image Generation—0
EINS: Long Short-Term Memory with Extrapolated Input Network Simplification—0
Exploring Cellular Protein Localization Through Semantic Image Synthesis—0
Semantic Hierarchy Emerges in the Deep Generative Representations for Scene Synthesis—0
Style-based Encoder Pre-training for Multi-modal Image Synthesis—0
InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers—0
Distortion Estimation Through Explicit Modeling of the Refractive Surface—0
Synthetic dataset generation for object-to-model deep learning in industrial applicationsCode0
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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