Top 5 convolutional neural network paper in 2022

Author: arxiv.org

Date Submitted: 10/20/2019 05:36 AM

Average star voting: 5 ⭐ ( 52722 reviews)

Summary: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Match with the search results: This document provides a brief introduction to CNNs, discussing recently published papers and newly formed techniques in developing these ……. read more

Author: insightsimaging.springeropen.com

Date Submitted: 08/14/2020 11:51 AM

Average star voting: 5 ⭐ ( 27810 reviews)

Summary: Convolutional Neural Networks are used to extract features from images (and videos), employing convolutions as their primary operator. Below you can find a continuously updating list of convolutional neural networks.

Match with the search results: Convolution is a specialized type of linear operation used for feature extraction, where a small array of numbers, called a kernel, is applied ……. read more

Author: journalofbigdata.springeropen.com

Date Submitted: 03/06/2019 06:21 PM

Average star voting: 5 ⭐ ( 83314 reviews)

Summary: Convolutional neural networks, or CNNs for short, form the backbone of many modern computer vision systems. This post will describe the origins of CNNs, starting from biological experiments of the 1950s. Simple and Complex Cells In 1959, David Hubel and Torsten Wiesel described “simple cells” and “complex cells” in the human visual cortex. They proposed that…

Match with the search results: In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural ……. read more

Author: proceedings.neurips.cc

Date Submitted: 05/14/2019 04:36 PM

Average star voting: 5 ⭐ ( 63732 reviews)

Summary: In high-speed train safety inspection, two changed images which are derived from corresponding parts of the same train and photographed at different times are needed to identify whether they are defects. The critical challenge of this change classification task is how to make a correct decision by using bitemporal images. In this paper, two convolutional neural networks are presented to perform this task. Distinct from traditional classification tasks which simply group each image into different categories, the two presented networks are capable of inherently detecting differences between two images and further identifying changes by using a pair of images. In doing so, even in the case that abnormal samples of specific components are unavailable in training, our networks remain capable to make inference as to whether they become abnormal using change information. This proposed method can be used for recognition or verification applications where decisions cannot be made with only one image (state). Equipped with deep learning, this method can address many challenging tasks of high-speed train safety inspection, in which conventional methods cannot work well. To further improve performance, a novel multishape training method is introduced. Extensive experiments demonstrate that the proposed methods perform well.

Match with the search results: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 ……. read more

Author: ieeexplore.ieee.org

Date Submitted: 05/14/2020 08:29 PM

Average star voting: 5 ⭐ ( 81867 reviews)

Summary: It is critical, but difficult, to catch the small variation in genomic or other kinds of data that differentiates phenotypes or categories. A plethora of data is available, but the information from its genes or elements is spread over arbitrarily, making it challenging to extract relevant details for identification. However, an arrangement of similar genes into clusters makes these differences more accessible and allows for robust identification of hidden mechanisms (e.g. pathways) than dealing with elements individually. Here we propose, DeepInsight, which converts non-image samples into a well-organized image-form. Thereby, the power of convolution neural network (CNN), including GPU utilization, can be realized for non-image samples. Furthermore, DeepInsight enables feature extraction through the application of CNN for non-image samples to seize imperative information and shown promising results. To our knowledge, this is the first work to apply CNN simultaneously on different kinds of non-image datasets: RNA-seq, vowels, text, and artificial.

Match with the search results: One of the most popular deep neural networks is the Convolutional Neural Network … In this paper we will explain and define all the elements and important ……. read more