vimarsana.com

Page 12 - Convolutional Neural Networks News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Meta-learning helps to estimate oceanic barrier layer structure

Meta-learning helps to estimate oceanic barrier layer structure
phys.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from phys.org Daily Mail and Mail on Sunday newspapers.

Arabian seaIndia generalUnited statesLos angelesQi jifengJifeng qiIndian oceanArtificial neural networksConvolutional neural networksEnvironmental research communications on septUniversity of californiaEnvironmental research communicationsYin baoshuChinese academyGated recurrent unitsSea surface temperature

Meta-Learning Aids in Estimating Oceanic Barrier Layer Structure

Meta-Learning Aids in Estimating Oceanic Barrier Layer Structure
miragenews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from miragenews.com Daily Mail and Mail on Sunday newspapers.

United statesLos angelesArabian seaIndia generalIndian oceanEnvironmental research communications on septArtificial neural networksConvolutional neural networksUniversity of californiaChinese academyGated recurrent unitsEnvironmental research communicationsSea surface temperatureSea surface salinitySea surface wind

Going Deeper with Recursive Convolutional Layers by Johan Chagnon, Markus Hagenbuchner et al

The development of Convolutional Neural Networks (CNNs) trends towards models with an ever growing number of Convolutional Layers (CLs) and increases the number of trainable parameters significantly. Such models are sensitive to these structural parameters, which implies that large models have to be carefully tuned using hyperparameter optimisation, a process that can be very time consuming. In this paper, we study the usage of Recursive Convolutional Layers (RCLs), a module relying on an algebraic feedback loop wrapped around a CL, which can replace any CL in CNNs. Using three publicly available datasets, CIFAR10, CIFAR100 and SVHN, and a simple model comprised of 4 RCLs, we compare its performances with those obtained by its feedforward counterpart, and exhibit some core properties and use-cases of RCLs. In particular, we show that RCLs can lead to models of better performances, and that reducing the number of modules from four to one lead to a decrease in accuracy of 3.5% on average

Convolutional neural networks cnnsConvolutional neural networksConvolutional layersRecursive convolutional layersConvolutional neural networkDynamic depthImage classificationRecursive neural network

An improved deep learning-based optimal object detection system from i by Satya Prakash Yadav, Muskan Jindal et al

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

Fast convolutional neural networks cnnsConvolutional neural networksFast convolutional neural networksOnly look onceSingle stage detectorFaster region based convolutional neural networksSingle shot detectorChess piece identificationAster region based convolutional neural networksr cnnObject detectionIngle stage detector ssdOu only look once yolo

From Pixels to Patterns: Image Analysis Techniques

In the age of visual information, understanding images is no longer just the domain of the human eye. With rapid advancements in technology, image analysis

Convolutional neural networks cnnsGray level co occurrence matrixConvolutional neural networks

vimarsana © 2020. All Rights Reserved.