A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This paper simplifies diffusion models for high resolution images.
SR-NAM maps low-res images to multiple high-res images realistically.
Paper generates high-resolution fashion images based on body pose.
StyleNeRF generates high-resolution images with 3D consistency and style control.
Deep learning for Venus images uses high-res hyperspectral data to simulate ground truth.
We propose Progressive Structure-conditional Generative Adversarial Networks (PSGAN), a new framework that can generate full-body and high-resolution character images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resol…
This work combines GANs and A3C for high-resolution image compression.
Deep learning speeds up whole heart MRI to 30 seconds.
Pixel-space diffusion models outperform latent models on high-resolution image synthesis.
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolut…
Generates high-resolution images from low-resolution inputs.
HRFA generates high-resolution, realistic adversarial examples for DNNs.
Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 r…
DL improves precipitation nowcasting from radar images.
Efficiently processes high res images by selecting relevant patches.
Study uses deep learning to detect BCCs in high-res histopathological images.
A new NAS framework optimizes 3D medical image segmentation architectures.
Paper introduces DACAL for high-resolution photo and video enhancement.
CoRAS adapts image acquisition rates for accurate reconstruction.
Purpose: To introduce a combined machine learning (ML) and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI), and demonstrate its application in high-resolution structural and diffusion imaging. Methods: Singleshot EPI is an efficient enco…
Deep learning improves 3D microscopy resolution without matched target images.
R2D2-GANs generate high-resolution images at real-time speed.
Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-…
Simple method detects deepfakes with few labeled samples.
SRFRN accelerates image super-resolution using shallow residual units.
Paper presents a GAN model for realistic river image synthesis.
GWHD dataset offers 4,700 high-res images of wheat heads.
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
Novel approach combines local and global brain changes for AD prediction.
We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality of its generated samples and improving itself accordingly. Its inference and generator models are jointly trained in an introspective way. On…
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial…
Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differe…
Neural networks have proven their capabilities by outperforming many other approaches on regression or classification tasks on various kinds of data. Other astonishing results have been achieved using neural nets as data generators, especially in settings of generative adversarial networks (GANs). One special applicati…
We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists. Each item is photographed from a variety of angles. We provide baseline results on 1) high-resolution image generation, and 2) image generation conditioned on the gi…
Deep learning method improves gene ontology classification of neural images.
A cost-effective method to generate high-resolution images using wavelet-based super-resolution.
In this paper, we propose a method using a three dimensional convolutional neural network (3-D-CNN) to fuse together multispectral (MS) and hyperspectral (HS) images to obtain a high resolution hyperspectral image. Dimensionality reduction of the hyperspectral image is performed prior to fusion in order to significantl…
A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral p…
Paper develops SKPD framework for signal region detection in image regression.
Improved autoencoder for realistic face manipulation.
Deep neural network models used for medical image segmentation are large because they are trained with high-resolution three-dimensional (3D) images. Graphics processing units (GPUs) are widely used to accelerate the trainings. However, the memory on a GPU is not large enough to train the models. A popular approach to …
Deep-learning improves 6x6-mm OCTA angiograms by reducing noise and artifacts.
We propose a novel denoising framework for task functional Magnetic Resonance Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we u…
We introduce a new, systematic framework for visualizing information flow in deep networks. Specifically, given any trained deep convolutional network model and a given test image, our method produces a compact support in the image domain that corresponds to a (high-resolution) feature that contributes to the given exp…
ViCE uses superpixels to enhance self-supervised learning for better dense visual embeddings.
Deep learning model detects landmarks in X-ray cephalograms.
Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such …
In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models. We present trainable deep neural networks for transforming images to adversarial perturbations. Our proposed models can produce ima…