Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
ContraGAN: Contrastive Learning for Conditional Image Generation
Conditional image generation is the task of generating diverse images using class label information. Although many conditional Generative Adversarial Networks (GAN) have shown realistic results, such methods consider pai…
Conditional Image GenerationContrastive LearningData AugmentationImage GenerationDeep Polynomial Neural Networks
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to…
Conditional Image GenerationFace IdentificationFace RecognitionFace Verification+4Training Generative Adversarial Networks with Limited Data
Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significant…
10-shot image generationConditional Image GenerationImage GenerationGANs in computer vision ebook
In this article-series we are reviewing the most fundamental works of Generative Adversarial Networks in Computer Vision. We start from the very beginning from concepts such as generative learning, adversarial learning. …
ArticlesConditional Image GenerationImage GenerationImage-to-Image Translation+2Reposing Humans by Warping 3D Features
We address the problem of reposing an image of a human into any desired novel pose. This conditional image-generation task requires reasoning about the 3D structure of the human, including self-occluded body parts. Most …
Conditional Image GenerationDecoderImage Generationperson reposing+1MISC: Multi-Condition Injection and Spatially-Adaptive Compositing for Conditional Person Image Synthesis
In this paper, we explore synthesizing person images with multiple conditions for various backgrounds. To this end, we propose a framework named "MISC" for conditional image generation and image compositing. For conditio…
Conditional Image GenerationImage GenerationConditional Image Generation and Manipulation for User-Specified Content
In recent years, Generative Adversarial Networks (GANs) have improved steadily towards generating increasingly impressive real-world images. It is useful to steer the image generation process for purposes such as content…
Conditional Image GenerationImage GenerationImage ManipulationText to Image Generation+1Evaluation Metrics for Conditional Image Generation
We present two new metrics for evaluating generative models in the class-conditional image generation setting. These metrics are obtained by generalizing the two most popular unconditional metrics: the Inception Score (I…
Conditional Image GenerationImage GenerationMelanoma Detection using Adversarial Training and Deep Transfer Learning
Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance o…
Conditional Image GenerationGeneral ClassificationImage GenerationImage-to-Image Translation+5Attentive Normalization for Conditional Image Generation
Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain. It is still not suff…
Conditional Image GenerationImage GenerationSemantic correspondenceSemantic Similarity+1Feature Quantization Improves GAN Training
The instability in GAN training has been a long-standing problem despite remarkable research efforts. We identify that instability issues stem from difficulties of performing feature matching with mini-batch statistics, …
Conditional Image GenerationFace GenerationImage GenerationImage-to-Image Translation+3Generative Latent Implicit Conditional Optimization when Learning from Small Sample
We revisit the long-standing problem of learning from a small sample, to which end we propose a novel method called GLICO (Generative Latent Implicit Conditional Optimization). GLICO learns a mapping from the training ex…
Conditional Image GenerationData Augmentationimage-classificationImage Classification+1Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images
Data augmentation can effectively resolve a scarcity of images when training machine-learning algorithms. It can make them more robust to unseen images. We present a lesion conditional Generative Adversarial Network LcGA…
Computed Tomography (CT)Conditional Image GenerationData AugmentationGenerative Adversarial Network+3BigGAN-based Bayesian reconstruction of natural images from human brain activity
In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is difficult, especially when reconstructing …
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationA U-Net Based Discriminator for Generative Adversarial Networks
Among the major remaining challenges for generative adversarial networks (GANs) is the capacity to synthesize globally and locally coherent images with object shapes and textures indistinguishable from real images. To ta…
Conditional Image GenerationData AugmentationImage GenerationWhen Relation Networks meet GANs: Relation GANs with Triplet Loss
Though recent research has achieved remarkable progress in generating realistic images with generative adversarial networks (GANs), the lack of training stability is still a lingering concern of most GANs, especially on …
Conditional Image GenerationImage GenerationRelationRelation Network+2Reconstructing the Noise Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising. Although the conditional image generation techniques have led to large improvements in this ta…
Conditional Image GenerationDenoisingImage DenoisingImage Generation+1OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering
We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) obje…
ClusteringConditional Image GenerationForeground SegmentationGenerative Adversarial Network+1Image Outpainting and Harmonization using Generative Adversarial Networks
Although the inherently ambiguous task of predicting what resides beyond all four edges of an image has rarely been explored before, we demonstrate that GANs hold powerful potential in producing reasonable extrapolations…
Conditional Image GenerationImage OutpaintingTriple Generative Adversarial Networks
We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifie…
ClassificationConditional Image GenerationData AugmentationGeneral Classification+3