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Meta-learning helps to estimate oceanic barrier layer structure

Meta-learning helps to estimate oceanic barrier layer structure
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Arabian Sea , India General , United States , Los Angeles , Qi Jifeng , Jifeng Qi , Indian Ocean , Artificial Neural Networks , Convolutional Neural Networks , Environmental Research Communications On Sept , University Of California , Environmental Research Communications , Yin Baoshu , Chinese Academy , Gated Recurrent Units , Sea Surface Temperature , Sea Surface Salinity , Sea Surface Wind ,

Meta-Learning Aids in Estimating Oceanic Barrier Layer Structure

Meta-Learning Aids in Estimating Oceanic Barrier Layer Structure
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United States , Los Angeles , Arabian Sea , India General , Indian Ocean , Environmental Research Communications On Sept , Artificial Neural Networks , Convolutional Neural Networks , University Of California , Chinese Academy , Gated Recurrent Units , Environmental Research Communications , Sea Surface Temperature , Sea Surface Salinity , Sea 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 Cnns , Convolutional Neural Networks , Convolutional Layers , Recursive Convolutional Layers , Convolutional Neural Network , Dynamic Depth , Image Classification , Recursive 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 Cnns , Convolutional Neural Networks , Fast Convolutional Neural Networks , Only Look Once , Single Stage Detector , Faster Region Based Convolutional Neural Networks , Single Shot Detector , Chess Piece Identification , Aster Region Based Convolutional Neural Networksr Cnn , Object Detection , Ingle Stage Detector Ssd , Ou 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 Cnns , Gray Level Co Occurrence Matrix , Convolutional Neural Networks ,