Deep generative model super|resolves spatially correlated ...

Attention-Enhanced Generative Adversarial Network for ... - OUCI

... model and investigate the high correlation of spatial context and spectral information in HSI. ... Wang, Hyperspectral Imagery Spatial Super-Resolution Using ...

Statistical Downscaling of Climate Datasets with Deep Generative ...

Statistical downscaling or super-resolution. (SR) with the deep-learning-based generative model might be a promising approach to address these challenges. It is ...

ChaofWang/Awesome-Super-Resolution - GitHub

Boosting Flow-based Generative Super-Resolution Models ... Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image ...

Hyperspectral Imagery Spatial Super-Resolution Using Generative ...

guide the generator to produce finer texture details. r Our HSSRGAN model can effectively extract more deep hierarchical spatial features and enhance coarse ...

‪Norihiro Oyama‬ - ‪Google Scholar‬

2019. Deep generative model super-resolves spatially correlated multiregional climate data. N Oyama, NN Ishizaki, S Koide, H Yoshida. Scientific Reports 13 (1) ...

Training Generative Image Super-Resolution Models by Wavelet ...

Flow and diffusion models perform stochastic sampling in the sense that a single model can generate many samples. Hence, they allow less control per sample on ...

Unsupervised deep learning for super-resolution reconstruction of ...

This indicates that GAN-based models reflect the spatial correlation between velocity components well, unlike the CNN model. We first ...

Physics-assisted generative adversarial network for X-ray tomography

... spatially correlated. Thus, one ... total, four variants of the deep generative model are investigated, in particular: a deep generative.

Deep Generative Decoder: MAP estimation of representations ...

We here present the Deep Generative Decoder (DGD), a simple generative model that computes model parameters and representations directly via maximum a ...

Super-resolution reconstruction of turbulent flow fields at various ...

... deep learning model ... High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative ...

A New Deep Generative Network for Unsupervised Remote Sensing ...

Even though the relevance of generating new spatial variations when super- resolving remotely sensed data in an unsupervised manner, this is, to ...

remote sensing - the NOAA Institutional Repository

In this study, the potential of a DCNN-based SISR model, called enhanced super-resolution generative adversarial network. (ESRGAN), to ...

Spatial and Angular Reconstruction of Light Field Based on Deep ...

We propose a generative network using high-dimensional convolution to improve both aspects. Our experimental results on both synthetic and real- ...

Adversarial super-resolution of climatological wind and solar data

Deep feed-forward neural networks have successfully learned how to model subgrid processes in cloud-resolving models (9). These models, among ...

Rewriting a Deep Generative Model - MIT

Recently, thanks to rapid advances of deep generative models [25,43,30], learning-based image synthesis and editing methods have become widely-used tools in the ...

Deep generative model embedding of single-cell RNA-Seq profiles ...

ScPhere resolves cell crowding, corrects multiple, complex batch factors, facilitates interactive visualization of large datasets, and ...

Improving Deep Generative Modeling with Applications - Zihang Dai

variable models that utilize latent factors to capture the correlation ... an LSTM based model [104], leading to super-human performances in some ...

An Introduction to Super Resolution using Deep Learning - Medium

This is often done by blurring or adding noise. Images of lower spatial resolution can also be scaled by a classic upsampling method such as ...

Deep Generative Models for Unsupervised Scale-Based and ...

... Spatially-Adaptive ... In this chapter we propose a deep autoencoder model that takes advantage of an adaptive resolution reconstruction loss.

MIT 6.S191 (2023): Deep Generative Modeling - YouTube

MIT Introduction to Deep Learning 6.S191: Lecture 4 Deep Generative Modeling Lecturer: Ava Amini 2023 Edition For all lectures, slides, ...