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Learning Graph Neural Networks with Approximate Gradient Descent
The dataset used in the paper is a graph neural network (GNN) dataset, where the goal is to learn a GNN with one hidden layer for node information convolution. -
ProGroTrack: Deep Learning-Assisted Tracking of Intracellular Protein Growth ...
The dataset used in this paper for tracking intracellular protein growth dynamics. -
UCM, NWPU-RESISC45, CLRS
Remote sensing image recognition datasets -
A Data-Centric Optimization Framework for Machine Learning
DaCeML is a Data-Centric Machine Learning framework that provides a simple, flexible, and customizable pipeline for optimizing training of arbitrary deep neural networks. -
Deep Autoencoder-based Fuzzy C-Means for Topic Detection
Topic detection is a process for determining topics from a collection of textual data. -
Deep learning for brain MRI segmentation: state of the art and future directions
Deep learning for brain MRI segmentation: state of the art and future directions. -
sEMG Dataset
The dataset used for training and testing the deep learning model for sEMG signal classification. -
Dataset for AI-Manipulated Face Detection
The dataset used for experiments, constructed by four categories: face entire synthesis, facial expression manipulation, facial attribute manipulation and identity manipulation. -
Predicting mrna abundance directly from genomic sequence using deep convoluti...
A dataset for predicting gene expression from genomic sequence using deep convolutional neural networks. -
MobileNet-2012
The dataset used in the paper is MobileNet-2012, a mobile image classification dataset. -
Unet++: A nested U-Net architecture for medical image segmentation
Unet++: A nested U-Net architecture for medical image segmentation. -
Frequency Centric Defense Mechanisms against Adversarial Examples
The proposed work uses the magnitude and phase of the Fourier Spectrum and the entropy of the image to defend against Adversarial Examples. -
MS-COCO and LAION-Aesthetics
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used MS-COCO and LAION-Aesthetics datasets. -
CIFAR-10, -20, -100, and ImageNet-1k
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, -20, -100, and ImageNet-1k datasets. -
MESA Atrial Fibrillation (AFib) Ancillary Study
The dataset used for training and testing the deep learning model for detecting enlarged perivascular spaces (ePVS) on brain MRI.