625 datasets found

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.

The dataset used in this paper for tracking intracellular protein growth dynamics.

The DIAMANT dataset is a medical image segmentation dataset used to evaluate the proposed DIAMANT architecture.

The Im2GPS dataset is a large-scale image dataset containing 2,000 images with associated GPS coordinates.

Remote sensing image recognition datasets

DaCeML is a Data-Centric Machine Learning framework that provides a simple, flexible, and customizable pipeline for optimizing training of arbitrary deep neural networks.

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.

The dataset used for training and testing the deep learning model for sEMG signal classification.

The dataset used for experiments, constructed by four categories: face entire synthesis, facial expression manipulation, facial attribute manipulation and identity manipulation.

A dataset for predicting gene expression from genomic sequence using deep convolutional neural networks.

The dataset used in the paper is MobileNet-2012, a mobile image classification dataset.

Unet++: A nested U-Net architecture for medical image segmentation.

The proposed work uses the magnitude and phase of the Fourier Spectrum and the entropy of the image to defend against Adversarial Examples.

The Flare7K++ dataset is an extension of the Flare7K dataset, consisting of 5,000 images of scattering flare and 2,000 images of reflective flare.

The Flare7K dataset is a synthetic dataset for lens flare removal, consisting of 5,000 images of scattering flare and 2,000 images of reflective flare.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used MS-COCO and LAION-Aesthetics datasets.

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.

The dataset used for training and testing the deep learning model for detecting enlarged perivascular spaces (ePVS) on brain MRI.

The Duke dataset is a large-scale person re-identification dataset, containing 702 identities with 16522 images.