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 6326–6350 of 6689 papers

TitleStatusHype
Invertible Residual NetworksCode1
Evaluating Textual Representations through Image Generation—0
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT—0
Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks—0
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis—0
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection—0
Variational learning across domains with triplet information—0
MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation—0
Investigating Object Compositionality in Generative Adversarial Networks—0
Skip-Thought GAN: Generating Text through Adversarial Training using Skip-Thought Vectors—0
Discriminator Rejection SamplingCode0
CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas—0
AutoLoss: Learning Discrete Schedules for Alternate OptimizationCode0
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANsCode1
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative ModelsCode1
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information FlowCode0
SALSA-TEXT : self attentive latent space based adversarial text generation—0
Large Scale GAN Training for High Fidelity Natural Image SynthesisCode1
Learning Neural Random Fields with Inclusive Auxiliary Generators—0
Adversarial Audio Super-Resolution with Unsupervised Feature Losses—0
DEEP ADVERSARIAL FORWARD MODEL—0
PA-GAN: Improving GAN Training by Progressive Augmentation—0
Generating Images from Sounds Using Multimodal Features and GANs—0
TopicGAN: Unsupervised Text Generation from Explainable Latent Topics—0
AutoLoss: Learning Discrete Schedule for Alternate Optimization—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