Papers Multimodal Unsupervised Image-To-Image Translation
“Multimodal Unsupervised Image-To-Image Translation” 태그가 달린 논문 17편 · 필터 해제
Wavelet-based Unsupervised Label-to-Image Translation
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generative Adversarial Networks (GANs) need a hu…
Image GenerationImage-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationTranslation+1A Style-aware Discriminator for Controllable Image Translation
Current image-to-image translations do not control the output domain beyond the classes used during training, nor do they interpolate between different domains well, leading to implausible results. This limitation largel…
Image ManipulationImage-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationUnsupervised Image-To-Image TranslationImage-to-image Translation via Hierarchical Style Disentanglement
Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation with diverse styles) tasks. However, du…
DisentanglementImage-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationTranslationBreaking the Cycle - Colleagues Are All You Need
This paper proposes a novel approach to performing image-to-image translation between unpaired domains. Rather than relying on a cycle constraint, our method takes advantage of collaboration between various GANs. This re…
AllImage-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationTranslation+1Lifespan Age Transformation Synthesis
We address the problem of single photo age progression and regression-the prediction of how a person might look in the future, or how they looked in the past. Most existing aging methods are limited to changing the textu…
Face Age EditingGenerative Adversarial NetworkHuman AgingImage Manipulation+4High-Resolution Daytime Translation Without Domain Labels
Modeling daytime changes in high resolution photographs, e.g., re-rendering the same scene under different illuminations typical for day, night, or dawn, is a challenging image manipulation task. We present the high-reso…
Image ManipulationImage Super-ResolutionImage-to-Image TranslationMultimodal Unsupervised Image-To-Image Translation+4StarGAN v2: Diverse Image Synthesis for Multiple Domains
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existi…
DiversityFundus to Angiography GenerationImage GenerationImage-to-Image Translation+2Breaking the cycle -- Colleagues are all you need
This paper proposes a novel approach to performing image-to-image translation between unpaired domains. Rather than relying on a cycle constraint, our method takes advantage of collaboration between various GANs. This re…
AllImage-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationTranslation+1Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fragmented. In this context, Generative Adv…
Data AugmentationGeneral ClassificationImage AugmentationImage Generation+3Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Most conditional generation tasks expect diverse outputs given a single conditional context. However, conditional generative adversarial networks (cGANs) often focus on the prior conditional information and ignore the in…
DiversityImage GenerationImage-to-Image TranslationMultimodal Unsupervised Image-To-Image Translation+1Semi-Supervised Image-to-Image Translation
Image-to-image translation is a long-established and a difficult problem in computer vision. In this paper we propose an adversarial based model for image-to-image translation. The regular deep neural-network based metho…
Generative Adversarial NetworkImage SegmentationImage-to-Image TranslationMultimodal Unsupervised Image-To-Image Translation+4Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation
In multimodal unsupervised image-to-image translation tasks, the goal is to translate an image from the source domain to many images in the target domain. We present a simple method that produces higher quality images th…
DisentanglementDiversityGenerative Adversarial NetworkImage-to-Image Translation+3Diverse Image-to-Image Translation via Disentangled Representations
Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for many applications: 1) the lack of aligned training pairs and 2) multiple possible outputs from a single i…
AttributeDiversityDomain AdaptationImage-to-Image Translation+4Multimodal Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the ta…
Image-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationTranslationUnsupervised Image-To-Image TranslationIn2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks
In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images. In this paper, we propose an extension of the unsupervised …
Generative Adversarial NetworkImage-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationTranslation+1Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks…
Image ColorizationImage-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationStyle Transfer+2Unsupervised Image-to-Image Translation Networks
Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of jo…
Domain AdaptationImage-to-Image TranslationMultimodal Unsupervised Image-To-Image TranslationTranslation+1