Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been achieved with numerous industrial applications. Most of these deep learning models rely on RGB images to localize and identify objects in the image. However in some a…
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48 results for “fast imaging”
New method speeds up image denoising models without sacrificing performance.
problem Efficiently training models for image denoising tasks.
method Introduces superkernel techniques for fast training of dense prediction models.
result Demonstrates effectiveness on SIDD+ benchmark with 6-8 RTX2080 GPU hours.
EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networkscs.LG
EM-GAN uses GANs for fast stress analysis of multi-segment interconnects.
problem Fast and accurate stress analysis for EM failure assessment in multi-segment interconnects.
method Conditional GAN model trained on images of multi-segment wires and current densities.
result EM-GAN provides accurate stress distribution with 6.6% error and 8.3X speedup.
Develops a fast method to analyze image similarities without pre-trained models.
problem Lack of efficient methods to analyze fine-level similarities in large image datasets.
method Wavelet decomposition and numerical analysis tools.
result Identifies similar images in standard datasets (like CIFAR) in a few seconds.
Paper proposes faster incremental subclass discriminant analysis.
problem Efficiently classify subclasses in incremental data.
method Exact and approximate linear and kernelized solutions.
result Superior training time and accuracy compared to existing methods.
The augmented Lagrangian (AL) method that solves convex optimization problems with linear constraints has drawn more attention recently in imaging applications due to its decomposable structure for composite cost functions and empirical fast convergence rate under weak conditions. However, for problems such as X-ray co…
We describe a new optimization scheme for finding high-quality correlation clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the optimal correlation clustering that are typically fast to compute and tight in practice. We demonstrate our a…
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stoch…
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learningstat.ML
This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e.g., the output is a photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initial…
In image deconvolution problems, the diagonalization of the underlying operators by means of the FFT usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather …
Fast amortized inference of neural activity from calcium imaging data with variational autoencodersstat.ML
Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are necessary to infer the true underlying spiking activity from fluorescence measurements. Bayesian model inversion can be used to solve this prob…
Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and fast modelling property. The technical essence lies in stochastically configuring hidden nodes (or basis functions) based on a supervisory m…
This survey analyzes deep learning methods for real-time semantic image segmentation.
problem Efficient and accurate semantic image segmentation for real-time applications.
method Comprehensive analysis of deep learning architectures and techniques for fast inference.
result Best-performing architectures and techniques for real-time semantic image segmentation.
Improves sampling speed of ARM models without losing accuracy.
problem Slow sampling from ARMs.
method Predictive sampling algorithm using ARM fixed-point iteration and learned forecasting modules.
result Significant improvements in sampling speed and efficiency.
Neural networks trained with backpropagation often struggle to identify classes that have been observed a small number of times. In applications where most class labels are rare, such as language modelling, this can become a performance bottleneck. One potential remedy is to augment the network with a fast-learning non…
Fast AutoAugmentcs.LG
Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment has been proposed as an algorithm to automatically search for augmentation policies from a dataset and has significantly enhanced performances on many image recognition tasks. However, its sea…
Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality. However, the longer reconstruction time of SIR methods hinders their use in X-ray CT in practice. To accelerate st…
RODEO speeds up MRI and CT image reconstruction from sparse data.
problem Real-time reconstruction of dynamic medical images from limited data.
method Autoencoder with robust l1-norm cost function and Split Bregman method.
result Real-time image reconstruction with minimal quality loss.
DIP-FAT improves adversarial training by diversifying perturbations.
problem Adversarial examples fool deep neural networks, leading to overfitting and poor performance.
method DIP-FAT uses random directions to diversify perturbations in adversarial training.
result DIP-FAT reduces overfitting and improves clean data accuracy.
Consistency Modelscs.LG
Consistency models generate high-quality samples fast and without iterative sampling.
problem Slow generation in diffusion models.
method Direct mapping of noise to data, supporting fast one-step generation and multistep sampling.
result Consistency models achieve state-of-the-art FID scores and outperform diffusion models in one-step generation.
We derive the fast convergence rates of a deep neural network (DNN) classifier with the rectified linear unit (ReLU) activation function learned using the hinge loss. We consider three cases for a true model: (1) a smooth decision boundary, (2) smooth conditional class probability, and (3) the margin condition (i.e., t…
A fast binary embedding method preserves Euclidean distances in high-dimensional data.
problem Preserving Euclidean distances in high-dimensional datasets.
method Stable noise-shaping quantization of with a sparse Gaussian random matrix, followed by a linear transformation.
result Euclidean distances are approximated by the norm on binary sequences, leading to accurate binary codes.
Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to …
The graph Laplacian is a standard tool in data science, machine learning, and image processing. The corresponding matrix inherits the complex structure of the underlying network and is in certain applications densely populated. This makes computations, in particular matrix-vector products, with the graph Laplacian a ha…
In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel d…
A fast compression-based similarity measure with applications to content-based image retrievalstat.ML
Compression-based similarity measures are effectively employed in applications on diverse data types with a basically parameter-free approach. Nevertheless, there are problems in applying these techniques to medium-to-large datasets which have been seldom addressed. This paper proposes a similarity measure based on com…
AugurOne trains single image generators without GANs using image warps.
problem Training end-to-end single image generators without GANs.
method Non-affine augmentations of single input images for training an upscaling neural network.
result End-to-end training yields state-of-the-art performance on conditional generation tasks.
Improved graph-based multiclass classification for multilayer data.
problem Efficient classification of multilayer data with limited labeled examples.
method Generalized diffuse interface methods applied to multilayer graphs, using spectral decomposition and fast matrix-vector products.
result Highly scalable and efficient classification for large, high-dimensional data sets.
Exemplar-based synthesis of geology using kernel discrepancies and generative neural networksstat.ML
We propose a framework for synthesis of geological images based on an exemplar image. We synthesize new realizations such that the discrepancy in the patch distribution between the realizations and the exemplar image is minimized. Such discrepancy is quantified using a kernel method for two-sample test called maximum m…
Purpose: To investigate the feasibility of myelin water content quantification using fast dual-echo steady-state (DESS) scans and machine learning with kernels. Methods: We optimized combinations of steady-state (SS) scans for precisely estimating the fast-relaxing signal fraction ff of a two-compartment signal model, …
Neural networks are prone to adversarial attacks. In general, such attacks deteriorate the quality of the input by either slightly modifying most of its pixels, or by occluding it with a patch. In this paper, we propose a method that keeps the image unchanged and only adds an adversarial framing on the border of the im…
Modeling the Point Spread Function (PSF) of wide-field surveys is vital for many astrophysical applications and cosmological probes including weak gravitational lensing. The PSF smears the image of any recorded object and therefore needs to be taken into account when inferring properties of galaxies from astronomical i…
Adversarial examples are intentionally perturbed images that mislead classifiers. These images can, however, be easily detected using denoising algorithms, when high-frequency spatial perturbations are used, or can be noticed by humans, when perturbations are large. In this paper, we propose EdgeFool, an adversarial im…
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and also shown potential to significantly speed up MR reconstruction with reduced measurements. This article gives an overview of deep learni…
The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations in order to approximately decompose and eventually invert such big…
Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have develo…
Symmetric nonnegative matrix factorization (NMF), a special but important class of the general NMF, is demonstrated to be useful for data analysis and in particular for various clustering tasks. Unfortunately, designing fast algorithms for Symmetric NMF is not as easy as for the nonsymmetric counterpart, the latter adm…
We propose and demonstrate the use of a model-assisted generative adversarial network (GAN) to produce fake images that accurately match true images through the variation of the parameters of the model that describes the features of the images. The generator learns the model parameter values that produce fake images th…
In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classifier by masking salient parts of the input image. Our model generalises well to unseen images and requires a single forward pass to perform …
WildCat efficiently compresses neural network attention mechanisms.
problem Expensive quadratic runtime of attention mechanisms in neural networks.
method Uses a weighted coreset with a fast subsampling algorithm to approximate attention with near-linear time complexity.
result Approximates exact attention with super-polynomial error decay and near-linear runtime.
CycleQSM uses deep learning to accurately map tissue magnetic susceptibility without needing paired data.
problem Accurately mapping magnetic susceptibility values from phase images using QSM.
method Unsupervised deep learning approach using physics-informed cycleGAN.
result The method provides more accurate QSM maps compared to existing deep learning approaches.
Glare is a phenomenon that occurs when the scene has a reflection of a light source or has one in it. This luminescence can hide useful information from the image, making text recognition virtually impossible. In this paper, we propose an approach to detect glare in images taken by users via mobile devices. Our method …
-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstructioneess.IV
Ensembled neural networks improve MRI image quality.
problem Accelerated parallel MR imaging with high SSIM scores.
method Ensembled -net combining parallel coil and sensitivity networks, trained with supervised and semi-supervised methods.
result Ensembling models achieves visually sharp and textured images with robust SSIM scores.
Magnetic resonance imaging (MRI) is known to be a slow imaging modality and undersampling in k-space has been used to increase the imaging speed. However, image reconstruction from undersampled k-space data is an ill-posed inverse problem. Iterative algorithms based on compressed sensing have been used to address the i…
Combines variational autoencoders with normalizing flows for faster training.
problem Training normalizing flow models like Glow is slow and requires deep architectures.
method Integrates Glow with a variational autoencoder to speed up training.
result The combined model achieves similar image quality and likelihood to Glow but trains faster.
Manifold learning has been successfully applied to a variety of medical imaging problems. Its use in real-time applications requires fast projection onto the low-dimensional space. To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimen…
A new method for fast optimal transport using sliced Wasserstein generalized geodesics.
problem Computing optimal transport distances efficiently and accurately.
method Proposes a new proxy of squared Wasserstein distance based on one-dimensional projections.
result min-SWGG is an upper bound of Wasserstein distance with similar computational complexity.
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities or prior information of predefined transforms, Deepcompl…