Convolutional GANs favor low spatial frequencies, affecting fine detail generation.
problem Understanding GANs' limitations in high spatial frequency learning.
method Proposed method to manipulate GANs' bias against high spatial frequencies.
result Convolutional GANs have a bias against learning high spatial frequencies.
Spatial GAN improves texture synthesis quality and scalability.
problem Texture synthesis quality and scalability issues.
method Spatial Generative Adversarial Networks (SGAN) with extended noise distribution.
result High quality textures, scalability, real-time generation, and fusion of diverse images.
Combines GANs and EVT for better modeling of spatial climate extremes.
problem Modeling dependencies between climate extremes, especially in high-dimensional spaces.
method Generative Adversarial Networks (GANs) combined with Extreme Value Theory (EVT).
result evtGAN outperforms classical GANs and statistical approaches in modeling spatial extremes.
Hybrid VAE-GAN model generates sequential data with spatial correlations.
problem Generating sequences with spatial correlations between frames.
method Semi-recurrent CNNs in encoder, generator, and discriminator; sampling subsequent frames from latent distributions.
result Maintains dependencies between frames in generated sequences.
COCO-GAN generates images by parts using spatial coordinates, achieving state-of-the-art quality.
problem Generating full images from partial observations due to computational constraints.
method COCO-GAN uses conditional coordination to generate images by parts based on spatial coordinates, with a generator and discriminator learning to justify realism.
result COCO-GAN produces state-of-the-art-quality full images during inference, enabling novel applications like beyond-boundary generation and panorama generation.
New methods improve GANs' conditional generation.
problem Improper conditioning in GANs limits their ability to generate images with specific attributes.
method Introducing two models: an information retrieving model and a spatial bilinear pooling model.
result Significantly enhanced log-likelihood of test data under conditional distributions.
Researchers develop tests to assess quality of GAN-generated images.
problem Lack of objective means to evaluate domain-relevant quality of GAN-generated images.
method Designed stochastic context models (SCMs) and statistical classifiers to detect high-order spatial arrangements in GAN-generated images.
result GANs can generate images that appear accurate visually but lack specific high-order spatial arrangements.
Physics-informed GANs estimate elastic moduli from mechanical tests.
problem Estimating spatially-varying elastic moduli from measured deformations.
method Physics-informed Generative Adversarial Networks (PI-GANs) with PDE constraints.
result Generated stiffness samples match true distribution statistics.
Improves deep generative models to generate images of any size.
problem Fixed-sized output images from deep generative models.
method Integrates spatial noise vectors into fully convolutional neural networks.
result Theoretical interpretation of infinite spatial generation using spatial stochastic processes.
DDPMs can reproduce medical image context, showing interpolation between samples.
problem Understanding DDPMs' ability to learn spatial context in medical imaging.
method Used stochastic context models (SCMs) to produce training data and assess DDPMs' performance.
result DDPMs can generate contextually correct images, interpolating between samples.
Paper introduces PSGAN for texture synthesis with manifold properties.
problem Texture synthesis with limited ability to handle diverse data.
method Generative adversarial networks (GAN) with extended noise tensor structure.
result PSGAN can smoothly interpolate and generate novel textures.
Model predicts unseen climate extremes to inform risk planning.
problem Missing unseen climate extremes in historical records.
method DeepX-GAN model capturing spatial dependence.
result Unseen heat extremes disproportionately threaten vulnerable regions.
Generative model learns to compose images of objects from different distributions.
problem Capturing complex interactions between objects in scenes.
method Composition-by-Decomposition (CoDe) network.
result Model generates realistic composite images capturing interactions between input objects.
Generates realistic pedestrian data for training detectors.
problem Lack of labeled pedestrian data for training detectors.
method Pedestrian-Synthesis-GAN using GAN with multiple discriminators and SPP layer.
result Synthetic pedestrians improve detector performance.
A new method uses GANs for robust optimization under uncertain data.
problem Optimizing supply chains under demand uncertainty with ambiguous distributions.
method Generative adversarial networks (GANs) for data-driven distributionally robust chance constrained programming.
result The approach effectively handles uncertain data distributions and improves supply chain optimization.
Proposes GANs using Capsule Networks for faster image synthesis.
problem Faster image synthesis with fewer training samples and epochs.
method Capsule Networks for image synthesis using GAN architectures.
result Learn data manifold faster and synthesize visually accurate images.
Y-GAN uses multi-camera data to estimate depth maps without expensive hardware.
problem Depth perception for autonomous systems requires accurate 3D spatial information.
method Proposes Y-GAN, a deep convolutional generative adversarial network.
result Y-GAN estimates depth maps from multi-camera stereo images without ground truth data.
New model predicts urban sprawl sensitivity from remote sensing data.
problem Forecasting urban sprawl sensitivity to economic factors.
method Physics-constrained conditional GANs for image-to-image translation.
result Model accurately predicts urban sprawl sensitivity without detailed data.
Physics-informed GANs model subsurface flow at the Hanford Site.
problem Uncertainty quantification for subsurface flow modeling at the Hanford Site.
method Physics-informed GANs, hierarchical domain parallelism, multiple GPUs, efficient communication.
result Highly scalable physics-informed GANs model subsurface flow on Summit supercomputer.
GANs generate biological cell images capturing protein relationships.
problem Synthesize cells from fluorescence microscopy images.
method Adapted GANs with casual dependencies for multi-channel image generation.
result Demonstrated ability to predict temporal evolution from static images.
DA-PredGAN uses GANs for accurate COVID-19 spread predictions and data assimilation.
problem Predicting and understanding the spread of COVID-19.
method Generative adversarial network (GAN) for predictions and data assimilation.
result DA-PredGAN accurately predicts and assimilates COVID-19 spread data.
MAD-GAN detects anomalies in multivariate time series data using GANs.
problem Complex multivariate time series data requires better anomaly detection methods.
method Unsupervised anomaly detection using Generative Adversarial Networks (GANs).
result MAD-GAN effectively detects anomalies in real-world CPS systems.
Seismic inversion method uses GAN to improve efficiency and accuracy.
problem Difficulty in combining geological knowledge with seismic data and assessing uncertainty.
method Generative Adversarial Network (GAN) for seismic inversion.
result GAN-generated models conform to observation data with low uncertainty.
Generative adversarial networks generate realistic, time-evolving high-resolution atmospheric fields.
problem Improving spatial resolution of low-resolution atmospheric images.
method Recurrent, stochastic super-resolution GAN for generating ensembles of time-evolving high-resolution atmospheric fields.
result The GAN produces realistic, temporally consistent super-resolution sequences for radar-measured precipitation and cloud optical thickness.
New method uses GAN to efficiently simulate geologic media.
problem Computational infeasibility of probabilistic inversion for high-dimensional problems.
method Spatial Generative Adversarial Network (SGAN) for geostatistical simulation.
result SGAN enables fast generation of geostatistical realizations for efficient inversion.
Method synthesizes 4D CMR images from XCAT model using GAN and SPADE.
problem Synthesizing realistic 4D CMR images with annotations and adaptable styles.
method Hybrid GAN approach with XCAT anatomical ground truth and SPADE for semantic preservation.
result Synthesized images with modality-specific features learned from real CMR data.
SAGAN improves image generation by leveraging attention across feature locations.
problem Traditional GANs generate details only from local feature maps, limiting image quality.
method SAGAN uses self-attention to model long-range dependencies and spectral normalization for better training.
result SAGAN achieves state-of-the-art results, boosting Inception score and reducing Frechet Inception distance.
Unified CLIP space manipulations improve GAN adaptation with a single target image.
problem Overfitting or underfitting in fine-tuning a pre-trained generator with a single target image.
method Two-step training strategy: latent optimization in CLIP space followed by generator fine-tuning with CLIP space consistency loss.
result Our model generates diverse outputs with the target texture and outperforms baseline models.
Method learns latent representations from spatio-temporal data.
problem Modeling dynamic data over time.
method Semi-supervised adversarial learning with GANs and RNNs.
result Competitive classification performance of latent representations.
SXL embeds spatial autocorrelation into neural networks for better geographic data learning.
problem Difficulties in learning spatial effects for neural networks in geographic data.
method SXL uses auxiliary tasks and autoregressive embeddings to learn spatial autocorrelation.
result SXL improves neural network training in unsupervised and supervised learning tasks.
Generative deep learning improves precipitation forecasts by adding resolution.
problem Inaccurate and unreliable precipitation forecasts due to unresolved processes.
method Applying GANs to super-resolve low-resolution weather model data using radar measurements.
result GANs and VAE-GANs produce high-resolution precipitation maps with better statistical properties than existing methods.
Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation have compelled insurance companies to adopt Monte Carlo (MC) simulations to valu…
Generative model generates images with multiple object classes.
problem Generating images with multiple object classes.
method Conditional Deep Convolutional GAN architecture, stabilized against collapse.
result System generates diverse samples with inter-object relationships.
Generative model for high-resolution video generation.
problem Challenges in generating high-resolution videos due to memory and training stability limitations.
method Progressive growing of sliced Wasserstein GANs (SWGAN) for incremental spatiotemporal information learning.
result Generated photorealistic face videos of 256x256x32 resolution with an inception score of 14.57.
Wasserstein GANs with Gradient Penalty compute a different optimal transport problem called congested transport.
problem Training generative models to produce high-quality synthetic data.
method Wasserstein GANs with Gradient Penalty (WGAN-GP) approach to calculate the Wasserstein 1 distance.
result WGAN-GP computes the minimum of the congested transport problem, not the Wasserstein 1 distance.
Generative model calibrates 3D battery cathode morphologies from 2D images.
problem Calibrate 3D morphologies of all-solid-state battery cathodes from 2D microscopy images.
method Combining GANs with excursion sets of Gaussian random fields.
result Calibrated digital twins enable systematic exploration of morphological scenarios.
Physics-informed semantic inpainting improves geostatistical modeling by incorporating indirect measurements.
problem Inferring heterogeneous geological fields from limited measurements and prior spatial statistics.
method Proposes a physics-informed semantic inpainting framework using WGAN-GP to incorporate both direct and indirect measurements.
result The method satisfies physical conservation laws and enhances inpainting performance compared to using only direct measurements.
Proposes a deep structured generative model for complex scenes.
problem Difficulty in generating images with complicated structures.
method Boosts GANs with structure information encoded by a stochastic and-or graph (sAOG).
result Successfully captures and generates images with complex structures.
Deep learning schemes improve turbulence simulation accuracy.
problem Improving DL schemes for accurate turbulence simulation.
method Training DL schemes on turbulence simulations to correct for inaccuracies.
result Dynamic NN schemes improve large-scale turbulence geometry.
PHom-GeM uses topological features to assess generative models.
problem Generative models produce chaotic distributions during training.
method Persistent Homology for Generative Models (PHom-GeM) minimizes an objective function between true and reconstructed distributions.
result PHom-GeM is a topological distance measure for generative models.
DSRGAN learns independent structure and rendering without tuple supervision.
problem Learning disentangled representation for natural image generation without tuple supervision.
method Introducing an auxiliary domain with a common underlying-structure space, and designing a parallel generative network with a common Progressive Rendering Architecture.
result DSRGAN significantly outperforms state-of-the-art methods in disentanglability.
Survey of GANs and autoencoders, addressing mode collapse and likelihood issues.
problem Addressing mode collapse and likelihood issues in GANs and autoencoders.
method Explains various GAN and autoencoder variants, their applications, and methods to resolve issues.
result Various methods to resolve mode collapse and improve likelihood in GANs and autoencoders.
TemperatureGAN generates hourly atmospheric temperature data with high fidelity.
problem Generating accurate hourly atmospheric temperature data for climate risk assessment.
method Generative Adversarial Network (GAN) conditioned on months, locations, and time periods.
result TemperatureGAN produces high-fidelity hourly atmospheric temperature data with good spatial and temporal consistency.
Generative model creates meal images from ingredient descriptions.
problem Synthesize photo-realistic meal images from ingredient descriptions.
method Attention-based ingredients-image association model, cycle-consistent constraint.
result Model generates meal images corresponding to ingredient descriptions.
A new GAN model α-GAN with tunable loss function addresses gradient vanishing and mode collapse issues.
problem Addressing vanishing gradients and mode collapse in GANs.
method Introduced a tunable GAN α-GAN using a supervised α-loss function. result Holistic understanding of α-GAN related to Arimoto divergence and convergence properties. Unbalanced GANs stabilize GAN training by pre-training the generator with VAE.
problem Stable training of GANs to avoid mode collapses and improve image quality.
method Pre-train GAN generator with VAE, balance generator and discriminator training, prevent discriminator's early convergence.
result Unbalanced GANs reduce mode collapses and outperform ordinary GANs in stability, convergence, and image quality.
Fisher GAN improves GAN training stability and efficiency.
problem Stable training of GANs with minimal data constraints.
method Integrates Fisher information into IPM framework for GAN training.
result Fisher GAN achieves stable and efficient training without compromising critic capacity.
GANs can approximate SDEs for large time steps.
problem Approximating SDEs for large time steps using GANs.
method Proposed a conditional GAN architecture to enable strong approximation of SDEs.
result Supervised GAN outperformed standard GAN and other schemes in strong error.