Conditional Image Generation
133 papers with code • 10 benchmarks • 8 datasets
Conditional image generation is the task of generating new images from a dataset conditional on their class.
( Image credit: PixelCNN++ )
Benchmarks
These leaderboards are used to track progress in Conditional Image Generation
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Libraries
Use these libraries to find Conditional Image Generation models and implementationsDatasets
CIFAR-10
MS COCO
CIFAR-100
Tiny ImageNet
ImageNet-LT
ArtBench-10 (32x32)
Most implemented papers
Conditional Image Generation with PixelCNN Decoders
openai/pixel-cnn
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NeurIPS 2016
This work explores conditional image generation with a new image density model based on the PixelCNN architecture.
cGANs with Projection Discriminator
pfnet-research/sngan_projection
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ICLR 2018
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
eladrich/pixel2style2pixel
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CVPR 2021
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
GANimation: Anatomically-aware Facial Animation from a Single Image
albertpumarola/GANimation
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ECCV 2018
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.
Making Convolutional Networks Shift-Invariant Again
The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling.
Hierarchical Text-Conditional Image Generation with CLIP Latents
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style.
Invertible Conditional GANs for image editing
Guim3/IcGAN
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Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions.
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
We propose to decompose instruction execution to goal prediction and action generation.
ArtGAN: Artwork Synthesis with Conditional Categorical GANs
This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics.
Twin Auxiliary Classifiers GAN
One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier.
