Browse SoTA > Computer Vision > Image Generation > Conditional Image Generation

Conditional Image Generation

59 papers with code · Computer Vision
Subtask of Image Generation

Conditional image generation is the task of generating new images from a dataset conditional on their class.

( Image credit: PixelCNN++ )

Benchmarks

Greatest papers with code

Improved Techniques for Training GANs

NeurIPS 2016 tensorflow/models

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.

CONDITIONAL IMAGE GENERATION SEMI-SUPERVISED IMAGE CLASSIFICATION

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

19 Nov 2015tensorflow/models

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.

CONDITIONAL IMAGE GENERATION IMAGE CLUSTERING UNSUPERVISED REPRESENTATION LEARNING

Self-Attention Generative Adversarial Networks

arXiv 2018 jantic/DeOldify

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.

CONDITIONAL IMAGE GENERATION

Improved Training of Wasserstein GANs

NeurIPS 2017 eriklindernoren/PyTorch-GAN

Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.

CONDITIONAL IMAGE GENERATION

Conditional Image Synthesis With Auxiliary Classifier GANs

ICML 2017 eriklindernoren/PyTorch-GAN

We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models.

CONDITIONAL IMAGE GENERATION IMAGE QUALITY ASSESSMENT

Making Convolutional Networks Shift-Invariant Again

25 Apr 2019rwightman/pytorch-image-models

The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling.

CLASSIFICATION CONSISTENCY CONDITIONAL IMAGE GENERATION

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

CVPR 2018 NVIDIA/pix2pixHD

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).

Ranked #3 on Image-to-Image Translation on Cityscapes Labels-to-Photo (Per-pixel Accuracy metric)

CONDITIONAL IMAGE GENERATION FUNDUS TO ANGIOGRAPHY GENERATION INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

Large Scale GAN Training for High Fidelity Natural Image Synthesis

ICLR 2019 ajbrock/BigGAN-PyTorch

Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.

CONDITIONAL IMAGE GENERATION

Conditional Image Generation with PixelCNN Decoders

NeurIPS 2016 openai/pixel-cnn

This work explores conditional image generation with a new image density model based on the PixelCNN architecture.

CONDITIONAL IMAGE GENERATION

High-Fidelity Image Generation With Fewer Labels

6 Mar 2019google/compare_gan

Deep generative models are becoming a cornerstone of modern machine learning.

CONDITIONAL IMAGE GENERATION