22 datasets found

The dataset used in the paper is FashionMNIST and CIFAR-10, which are commonly used datasets for image classification tasks.

The dataset used in this paper is a pre-trained DNN trained on the Cifar10 and Cifar100 datasets.

The dataset used in the paper is ResNet-20, a deep convolutional neural network for image classification.

The DenseNet dataset is a dataset for image recognition on handwritten digits composed of 60,000 training data and 10,000 test images.

The dataset consists of 60,000 images of objects in 200 categories, with 300 images per category.

Convolution is the main building block of convolutional neural networks (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels...

The proposed model consists of three components: 1) Coordinate base computation: uses a 2D coordinate grid as input and generates the bases, 2) Image feature computation:...

Deep networks for image classification often rely more on texture information than object shape. This paper presents a deep-learning model inspired by geometric moments, a...

The dataset used in the paper to evaluate the alignment of deep neural networks with human perception.

Customer temporal behavioral data represented as images to perform churn prediction by leveraging deep learning architectures prominent in image classification.

Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc. Whilst some of this data is naturally dense (for...

The proposed framework for image classification tasks, using a hypersphere representation of deep features.

The dataset used in the paper is MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, and VGG-like.

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

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

A survey of the foundations, selected improvements, and some current applications of Deep Convolutional Neural Networks (CNNs).

Deep Neural Networks (DNNs) are universal function approximators providing state-of-the-art solutions on wide range of applications. Common perceptual tasks such as speech...

The dataset used in this paper is a MobileNetV2 model, which is a type of deep neural network. The dataset is used to evaluate the performance of the proposed heterogeneous system.

The dataset used in this paper is the WRN28x10 dataset, a deep neural network trained on the CIFAR-10 and CIFAR-100 datasets.

The dataset used in this paper is the VGG16 dataset, a deep neural network trained on the CIFAR-10 and CIFAR-100 datasets.