Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
Twin Auxiliary Classifiers GAN
Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the l…
Conditional Image GenerationDiversityImage GenerationcGANs with Conditional Convolution Layer
Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator. However, in the conventional cGANs techniques, it is still challenging for th…
Conditional Image GenerationImage GenerationSpatially Constrained GAN for Face and Fashion Synthesis
Image generation has raised tremendous attention in both academic and industrial areas, especially for the conditional and target-oriented image generation, such as criminal portrait and fashion design. Although the curr…
AttributeConditional Image GenerationFashion SynthesisGenerative Adversarial Network+2Making Convolutional Networks Shift-Invariant Again
Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and aver…
Classification ConsistencyConditional Image GenerationDomain GeneralizationImage Classification+1Normalized Diversification
Generating diverse yet specific data is the goal of the generative adversarial network (GAN), but it suffers from the problem of mode collapse. We introduce the concept of normalized diversity which force the model to pr…
Conditional Image GenerationDiversityGenerative Adversarial NetworkHand Pose Estimation+3Reference-based Variational Autoencoders
Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically r…
AttributeConditional Image GenerationImage GenerationHigh-Fidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images…
Conditional Image GenerationImage GenerationVocal Bursts Intensity PredictionHarmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation
Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely accounted to the success of conditional GAN models, which are often optimized by the joint use o…
Conditional Image GenerationDiversityImage GenerationImage Inpainting+3A Layer-Based Sequential Framework for Scene Generation with GANs
The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computer…
Conditional Image GenerationImage GenerationScene GenerationVirtual Conditional Generative Adversarial Networks
When trained on multimodal image datasets, normal Generative Adversarial Networks (GANs) are usually outperformed by class-conditional GANs and ensemble GANs, but conditional GANs is restricted to labeled datasets and en…
ClusteringConditional Image GenerationDecoderImage GenerationLearning Disentangled Representations with Reference-Based Variational Autoencoders
Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically r…
AttributeConditional Image GenerationImage GenerationGenerating Multiple Objects at Spatially Distinct Locations
Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions. Furthermore, conditional …
Conditional Image GenerationImage GenerationObjectText-to-Image GenerationShape-conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data
Conditional image generation is effective for diverse tasks including training data synthesis for learning-based computer vision. However, despite the recent advances in generative adversarial networks (GANs), it is stil…
Conditional Image GenerationImage GenerationObjectClass-Distinct and Class-Mutual Image Generation with GANs
Class-conditional extensions of generative adversarial networks (GANs), such as auxiliary classifier GAN (AC-GAN) and conditional GAN (cGAN), have garnered attention owing to their ability to decompose representations in…
Conditional Image GenerationImage GenerationImage-to-Image TranslationSpecificityFineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories. To disentangle the facto…
Conditional Image GenerationDisentanglementFine-Grained Visual CategorizationImage Clustering+1Large Scale GAN Training for High Fidelity Natural Image Synthesis
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial …
Conditional Image GenerationImage GenerationVocal Bursts Intensity PredictionMapping Instructions to Actions in 3D Environments with Visual Goal Prediction
We propose to decompose instruction execution to goal prediction and action generation. We design a model that maps raw visual observations to goals using LINGUNET, a language-conditioned image generation network, and th…
Action GenerationConditional Image GenerationImage GenerationInstruction FollowingDiverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes
Recent advances in Deep Learning and probabilistic modeling have led to strong improvements in generative models for images. On the one hand, Generative Adversarial Networks (GANs) have contributed a highly effective adv…
Conditional Image GenerationDiversityImage GenerationregressionGANimation: Anatomically-aware Facial Animation from a Single Image
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis. The most successful architecture is StarGAN, that conditions GANs generation process with i…
Conditional Image GenerationFace GenerationImage-to-Image TranslationUnsupervised Learning of Object Landmarks through Conditional Image Generation
We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision. We cast this as the problem of generating images that combine the appearanc…
Conditional Image GenerationImage GenerationObjectUnsupervised Facial Landmark Detection