Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
The Neural Painter: Multi-Turn Image Generation
In this work we combine two research threads from Vision/ Graphics and Natural Language Processing to formulate an image generation task conditioned on attributes in a multi-turn setting. By multiturn, we mean the image …
BenchmarkingConditional Image GenerationImage GenerationRobust Conditional Generative Adversarial Networks
Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision. The major focus so far has been on performance im…
Conditional Image GenerationImage GenerationSelf-Attention Generative Adversarial Networks
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-r…
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationText to Image Synthesis Using Generative Adversarial Networks
Generating images from natural language is one of the primary applications of recent conditional generative models. Besides testing our ability to model conditional, highly dimensional distributions, text to image synthe…
Conditional Image GenerationImage GenerationSentenceA Variational U-Net for Conditional Appearance and Shape Generation
Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intric…
Conditional Image GenerationImage GenerationImage Generation and Translation with Disentangled Representations
Generative models have made significant progress in the tasks of modeling complex data distributions such as natural images. The introduction of Generative Adversarial Networks (GANs) and auto-encoders lead to the possib…
Conditional Image GenerationFace GenerationImage GenerationImage-to-Image Translation+2BRUNO: A Deep Recurrent Model for Exchangeable Data
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provably exchangeable, meaning that the joint …
Anomaly DetectionBayesian InferenceConditional Image GenerationFew-Shot Learning+2cGANs with Projection Discriminator
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 approa…
Conditional Image GenerationImage GenerationSuper-ResolutionSemi-supervised FusedGAN for Conditional Image Generation
We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation mod…
AttributeConditional Image GenerationDiversityFace Generation+2High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
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 ap…
Conditional Image GenerationFundus to Angiography GenerationImage GenerationImage-to-Image Translation+5Class-Splitting Generative Adversarial Networks
Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i.e. in the conditional GAN setup. This is still observed for the recently proposed Wasserst…
ClusteringConditional Image GenerationImage GenerationImproved ArtGAN for Conditional Synthesis of Natural Image and Artwork
This paper proposes a series of new approaches to improve Generative Adversarial Network (GAN) for conditional image synthesis and we name the proposed model as ArtGAN. One of the key innovation of ArtGAN is that, the gr…
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationGeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
Object Transfiguration replaces an object in an image with another object from a second image. For example it can perform tasks like "putting exactly those eyeglasses from image A on the nose of the person in image B". U…
AttributeConditional Image GenerationImage GenerationObjectImproved Training of Wasserstein GANs
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 s…
Conditional Image GenerationImage GenerationSynthetic Data GenerationGP-GAN: Towards Realistic High-Resolution Image Blending
It is common but challenging to address high-resolution image blending in the automatic photo editing application. In this paper, we would like to focus on solving the problem of high-resolution image blending, where the…
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationVocal Bursts Intensity PredictionParallel Multiscale Autoregressive Density Estimation
PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by c…
Conditional Image GenerationDensity EstimationImage CompressionImage Generation+1ArtGAN: 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. This is i…
Art AnalysisConditional Image GenerationImage GenerationStacked Generative Adversarial Networks
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 consis…
Conditional Image GenerationInvertible Conditional GANs for image editing
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…
Conditional Image GenerationImage-to-Image TranslationLearning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so…
Conditional Image Generation