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

Showing 66266650 of 6689 papers

TitleStatusHype
Professor Forcing: A New Algorithm for Training Recurrent NetworksCode0
Learning What and Where to Draw0
Rain structure transfer using an exemplar rain image for synthetic rain image generation0
Example-Based Image Synthesis via Randomized Patch-Matching0
Neural Photo Editing with Introspective Adversarial NetworksCode0
Generating Synthetic Data for Text RecognitionCode0
Instance Normalization: The Missing Ingredient for Fast StylizationCode0
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation0
Interactive Illumination Invariance0
Generating Images Part by Part with Composite Generative Adversarial Networks0
Adversarial Training For Sketch Retrieval0
Conditional Image Generation with PixelCNN DecodersCode0
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsCode1
Improved Techniques for Training GANsCode1
Incorporating long-range consistency in CNN-based texture generationCode1
Adversarially Learned InferenceCode0
Laplacian Patch-Based Image Synthesis0
Image Style Transfer Using Convolutional Neural NetworksCode0
Density estimation using Real NVPCode1
Generative Adversarial Text to Image SynthesisCode1
Neural Autoregressive Distribution EstimationCode0
Training \& Quality Assessment of an Optical Character Recognition Model for Northern Haida0
Towards Conceptual CompressionCode0
Pixel-Level Domain TransferCode0
Appearance Harmonization for Single Image Shadow Removal0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified