Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
cGANs with Multi-Hinge Loss
We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised…
Conditional Image GenerationInfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. However, conditioning CNFs on signals of inter…
Conditional Image GenerationImage GenerationTime SeriesTime Series AnalysisControlling Style and Semantics in Weakly-Supervised Image Generation
We propose a weakly-supervised approach for conditional image generation of complex scenes where a user has fine control over objects appearing in the scene. We exploit sparse semantic maps to control object shapes and c…
Conditional Image GenerationImage GenerationcFineGAN: Unsupervised multi-conditional fine-grained image generation
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves the texture of one and the shape of the…
Conditional Image GenerationImage GenerationAnalyzing and Improving the Image Quality of StyleGAN
The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in bo…
AttributeConditional Image GenerationImage GenerationLOGAN: Latent Optimisation for Generative Adversarial Networks
Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with dropped modes. In this work, we improve CS-…
Conditional Image GenerationImage GenerationTwin Auxilary Classifiers GAN
Conditional generative models enjoy significant progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN) that generates highly discriminative images by extending the lo…
Conditional Image GenerationDiversityImage GenerationYour Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
We introduce a new local sparse attention layer that preserves two-dimensional geometry and locality. We show that by just replacing the dense attention layer of SAGAN with our construction, we obtain very significant FI…
Conditional Image GenerationDeep AttentionImage GenerationMixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We bui…
Conditional Image GenerationDisentanglementImage GenerationSemantic Bottleneck Scene Generation
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottleneck GAN model for unconditional synthe…
Conditional Image GenerationImage GenerationImage-to-Image TranslationScene Generation+1Consistency Regularization for Generative Adversarial Networks
Generative Adversarial Networks (GANs) are known to be difficult to train, despite considerable research effort. Several regularization techniques for stabilizing training have been proposed, but they introduce non-trivi…
Conditional Image GenerationImage GenerationUnconditional Image GenerationTRB: A Novel Triplet Representation for Understanding 2D Human Body
Human pose and shape are two important components of 2D human body. However, how to efficiently represent both of them in images is still an open question. In this paper, we propose the Triplet Representation for Body (T…
Conditional Image GenerationImage GenerationOpen-Ended Question AnsweringTripletRGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis
Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel …
Conditional Image GenerationImage GenerationRepresentation LearningStochastic Conditional Generative Networks with Basis Decomposition
While generative adversarial networks (GANs) have revolutionized machine learning, a number of open questions remain to fully understand them and exploit their power. One of these questions is how to efficiently achieve …
Conditional Image GenerationDiversityImage GenerationInfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers
Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. However, conditioning CNFs on signals of inter…
Conditional Image GenerationImage GenerationTime SeriesTime Series AnalysisTorchGAN: A Flexible Framework for GAN Training and Evaluation
TorchGAN is a PyTorch based framework for writing succinct and comprehensible code for training and evaluation of Generative Adversarial Networks. The framework's modular design allows effortless customization of the mod…
Conditional Image GenerationImage GenerationFlexible Conditional Image Generation of Missing Data with Learned Mental Maps
Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic. In clinical practice it is frequently common to acquire only sparse data (e.g.…
AnatomyConditional Image GenerationDecision MakingDiagnostic+2Generating High-Resolution Fashion Model Images Wearing Custom Outfits
Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determined set of outfits. In this paper, we br…
ArticlesConditional Image GenerationVocal Bursts Intensity PredictionAn Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Batch normalization has been widely used to improve optimization in deep neural networks. While the uncertainty in batch statistics can act as a regularizer, using these dataset statistics specific to the training set im…
Conditional Image GenerationFew-Shot LearningImage GenerationQuestion Answering+2Lifelong GAN: Continual Learning for Conditional Image Generation
Lifelong learning is challenging for deep neural networks due to their susceptibility to catastrophic forgetting. Catastrophic forgetting occurs when a trained network is not able to maintain its ability to accomplish pr…
Conditional Image GenerationContinual LearningImage GenerationKnowledge Distillation+1