Search Results for author:
Found 26 papers, 8 papers with code
Compressive Ptychography using Deep Image and Generative Priors
However, reconstructions with less number of scan points lead to imaging artifacts and significant distortions, hindering a quantitative evaluation of the results.
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space
Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature.
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes
Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks.
Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks
Lack of explainability in artificial intelligence, specifically deep neural networks, remains a bottleneck for implementing models in practice.
Removing Blocking Artifacts in Video Streams Using Event Cameras
In this paper, we propose EveRestNet, a convolutional neural network designed to remove blocking artifacts in videostreams using events from neuromorphic sensors.
Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications
Via novel optical designs, the 2D detector samples the HD data in a {\em compressive} manner; following this, algorithms are employed to reconstruct the desired HD data-cube.
SkinScan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and Documentation
The utilization of computational photography becomes increasingly essential in the medical field.
E3D: Event-Based 3D Shape Reconstruction
We first introduce an event-to-silhouette (E2S) neural network module to transform a stack of event frames to the corresponding silhouettes, with additional neural branches for camera pose regression.
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
Computed tomography is widely used to examine internal structures in a non-destructive manner.
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures.
Examining the Benefits of Capsule Neural Networks
Capsule networks are a recently developed class of neural networks that potentially address some of the deficiencies with traditional convolutional neural networks.
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
We apply our method on the problem of fine-tuning for unseen image formation models and on removal of artifacts introduced by GANs.
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting.
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Results: The proposed algorithm is validated on the 2018 PhysioNet challenge dataset.
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions.
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
We propose an XRF image inpainting approach to address the issue of long scanning time, thus speeding up the scanning process while still maintaining the possibility to reconstruct a high quality XRF image.
Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease.
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Finally, we show that our proposed model, the VSRResFeatGAN model, outperforms current state-of-the-art SR models, both quantitatively and qualitatively.
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks.
Efficient Video Object Segmentation via Network Modulation
Video object segmentation targets at segmenting a specific object throughout a video sequence, given only an annotated first frame.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
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Deep Multi-view Models for Glitch Classification
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO.
Compressive Holographic Video
Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image.
DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing
In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing.
Robust and Low-Rank Representation for Fast Face Identification with Occlusions
In this paper we propose an iterative method to address the face identification problem with block occlusions.
Deep Fully-Connected Networks for Video Compressive Sensing
In this work we present a deep learning framework for video compressive sensing.
Sparse Bayesian Methods for Low-Rank Matrix Estimation
Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesting practical applications.
