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A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm
Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more... -
Training a U-Net based on a random mode-coupling matrix to recover acoustic i...
A U-Net is trained to recover acoustic interference striations (AISs) from distorted ones. A random mode-coupling matrix model is introduced to generate a large number of... -
Haar Graph Pooling
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which... -
Deep Image Harmonization
Deep Image Harmonization. -
Ferdowsi University of Mashhad’s Pulmonary Embolism (FUMPE) dataset
A dataset for computer-aided diagnosis of pulmonary embolism using computed tomography pulmonary angiography images. -
FashionMNIST dataset
The dataset used in this paper is the FashionMNIST dataset, which consists of 60,000 images of clothing items from 10 different classes. -
AI4Arctic Sea Ice Challenge Dataset
The AI4Arctic Sea Ice Challenge Dataset is a benchmark for deep learning-based sea ice mapping. It provides a large-scale dataset for training and testing sea ice classification... -
PointConv: Deep Convolutional Networks on 3D Point Clouds
3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. PointConv can be applied on point clouds to build deep convolutional networks. -
Deep marching tetrahedra: a hybrid representation for high-resolution 3D shap...
Deep marching tetrahedra: a hybrid representation for high-resolution 3D shape synthesis -
WCE curated colon disease dataset for deep learning
WCE curated colon disease dataset for deep learning -
Deep unsupervised learning using nonequilibrium thermodynamics
Deep unsupervised learning using nonequilibrium thermodynamics -
Deep learning for decoding of linear codes-a syndrome-based approach
Deep learning for decoding of linear codes - a syndrome-based approach -
Error Correction Code Transformer
Error correction code transformer for decoding linear codes -
Sentinel-2 Sea-Ice Classification
Sentinel-2 sea ice classification dataset for training a deep learning model to classify polar sea ice as thick/snow-covered, young/thin, or open water. -
Occluded CIFAR
The dataset used in the paper is Occluded CIFAR. -
Cluttered MNIST and CIFAR-10
The dataset used in the paper is Cluttered MNIST and CIFAR-10. -
VGG Network E
The dataset used in this paper is the VGG Network E, a deep convolutional neural network for image recognition. -
CIFAR10 and ImageNet
The dataset used in the paper to evaluate the alignment of deep neural networks with human perception.
