564 datasets found

The dataset used in this paper is the ConvNeXt-T and ConvNeXt-S datasets, which are variants of the ConvNeXt model.

The dataset used in this paper is the ResNet-20 and ResNet-32 datasets, which are variants of the ResNet-50 model.

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

The dataset used in the paper is Pascal Voc 2012 and Cityscapes.

Density estimation is one of the most widely used methods for crowd counting in which a deep learning model learns from head-annotated crowd images to estimate crowd density in...

The Landslide4Sense competition provides a landslide benchmark data set with globally distributed multi-source satellite imagery.

The dataset used in the paper is the ResNet-50 dataset, a convolutional neural network model.

MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model.

The WHAM! dataset is used for testing the proposed Bayesian factorised speaker-environment adaptive training and test time adaptation approach for Conformer models.

The dataset used in the paper is DVSGesture, a dataset of 1,342 instances of 11 hand and arm gestures.

The dataset is used to test the proposed generalized residue network (gResNet) framework for learning unknown governing equations from observational data.

The dataset used in this work is a large test dataset containing 32,000 real and fake images from 18 models.

Deep learning has shown impressive performance on challenging perceptual tasks and has been widely used in software to provide intelligent services. However, researchers found...

The dataset used in the paper is the CIFAR-10 and SVHN datasets. CIFAR-10 is a dataset of 32x32 color images in 10 classes, while SVHN is a dataset of 32x32 color images of...

Gastrointestinal mucosal changes can cause cancers after some years and early diagnosing them can be very useful to prevent cancers and early treatment.

The MNIST and CIFAR-10 datasets are used to test the theory suggesting the existence of many saddle points in high-dimensional functions.

The dataset used in the paper is the AlexNet dataset, which contains 60,000 32x32 color images in 10 classes, with 6,000 images per class.

The dataset contains images processed by image restoration and enhancement methods, including deep learning-based methods.

Gaussian processes (GPs) are non-parametric, flexible models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially...

The dataset used for pothole detection using deep learning