One of the most promising techniques used in various sciences is deep neural networks
(DNNs). A special type of DNN called a convolutional neural network (CNN) consists of …

Convolutional neural networks (CNN) is a specialized case of artificial neural networks
(ANN) and finds its application in computer vision and parallel distributed computing for …

Abstract Convolutional Neural Networks (CNNs) use pooling to decrease the size of
activation maps. This process is crucial to increase the receptive fields and to reduce …

Abstract Visual Semantic Embedding (VSE) is a dominant approach for vision-language
retrieval, which aims at learning a deep embedding space such that visual data are …

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to
downscale feature maps for larger receptive fields and less memory consumption. However …

According to the World Health Organization statistics, as of 25 October 2022, there have
been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide …

Max-and average-pooling are the most popular pooling methods for downsampling in
convolutional neural networks. In this paper, we compare different pooling methods that …

The purpose of audio-text retrieval is to learn a cross-modal similarity function between
audio and text, enabling a given audio/text to find similar text/audio from a candidate set …

With the increasing demand for deep learning in the last few years, CNNs have been widely
used in many applications and have gained interest in classification, regression, and image …

Data compression at the Internet of Things (IoT) edge node aims to minimize data traffic in
smart cities. The traditional Huffman Coding Algorithm (HCA) is shown as the most effective …