11 datasets found

The dataset used in the paper is ResNet-20 and ResNet-SVM-20, two types of convolutional neural networks.

Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control.

The dataset used in this paper is the SqueezeNet v1.1 and ZynqNet CNNs, which are characterized by small model size and limited computational requirements.

This paper proposes a convolutional neural network hardware accelerator based on field programmable logic gate array (FPGA).

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

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.

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

The dataset used in this paper is a Bottleneck layer, which is a type of convolutional neural network layer. The dataset is used to evaluate the performance of the proposed...

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.

Single Image Super-Resolution (SR) aims to generate a High Resolution (HR) image I SR from a low resolution (LR) im-age I LR such that it is similar to original HR image I HR....

The CIFAR-10 dataset consists of 60,000 training images and 10,000 test images. Each image is a 32×32 color image.