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Conditional Image Generation

21 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.

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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. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic.

CONDITIONAL IMAGE GENERATION

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. Comparatively, unsupervised learning with CNNs has received less attention.

CONDITIONAL IMAGE GENERATION UNSUPERVISED REPRESENTATION LEARNING

Self-Attention Generative Adversarial Networks

21 May 2018jantic/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. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps.

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). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic.

CONDITIONAL IMAGE GENERATION IMAGE-TO-IMAGE TRANSLATION INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

Improved Training of Wasserstein GANs

NeurIPS 2017 igul222/improved_wgan_training

Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge.

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. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks.

CONDITIONAL IMAGE GENERATION

Conditional Image Synthesis With Auxiliary Classifier GANs

ICML 2017 kaonashi-tyc/zi2zi

Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. 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

cGANs with Projection Discriminator

ICLR 2018 pfnet-research/sngan_projection

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. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors.

CONDITIONAL IMAGE GENERATION SUPER RESOLUTION

Invertible Conditional GANs for image editing

19 Nov 2016LynnHo/AttGAN-Tensorflow

Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images.

CONDITIONAL IMAGE GENERATION IMAGE-TO-IMAGE TRANSLATION

Stacked Generative Adversarial Networks

CVPR 2017 xunhuang1995/SGAN

In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations.

CONDITIONAL IMAGE GENERATION