Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

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We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...

A method for enforcing smoothness constraints on surface meshes produced by a Graph Convolutional Neural Network (GCNN) including the steps of reading image data from a memory, the image data including two-dimensional image data representing a three-dimens ...

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-supervised learning models fail to leverage information across multiple data t ...

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously appeared impossible, such as human-level object recognition, text synthesis, translation, playing games and many more. In spite of these major achievements, o ...

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspec ...

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively e ...

Multiple sclerosis (MS) is the most common demyelinating disease of the central nervous system and affects almost 3 million people worldwide. There is currently no cure for MS, and its symptoms, starting with fatigue and weakness, often progress over time ...

The COVID-19 pandemic outbreak is causing a dramatic worsening in the already complicated living conditions of blind and visually impaired individuals. Social distancing is the most effective strategy to limit virus spread, but is extremely difficult for b ...

Objective To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with ...

Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration ...

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switch ...

Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as image recognition, object detection, and semantic segmentation. Even though the discriminative power of DNNs is nowadays unquestionable, serious concerns have ...

In this study, we present the deep learning image segmentation model for drone-based grain size analysis of gravel bars called GALET. The objectives are to quantify the performance of the code and to test its applicability in river research and management. ...

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

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Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

A method and system for enforcing smoothness constraints on surface meshes from a graph convolutional neural network

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

XTab: Cross-table Pretraining for Tabular Transformers

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Theory of Deep Learning: Neural Tangent Kernel and Beyond

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

Self-supervised Segmentation via Background Inpainting

Deep learning-based analysis of multiple sclerosis lesions with high and ultra-high field MRI

Deploying an Instance Segmentation Algorithm to Implement Social Distancing for Prosthetic Vision

Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

Understanding Deep Neural Networks using Adversarial Attacks

GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars