The optimization technique further improves the results i.e. F1 score improves from 0.978 to 0.982 for ResNet-101. These results verify the proposed method's ability to recognize teeth with high degree of accur
Deep learning-based solution The forecasting accuracy was enhanced using neural networks with asymmetric evolution using standard adaptation21. The adaptive technique improves accuracy by examining the scope of possible responses from many viewpoints and applying various potential answers. In contrast to grad...
An in-depth explanation of Gradient Descent and how to avoid the problems of local minima and saddle points.
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In machine learning (ML), a gradient is a vector that gives the direction of the steepest ascent of the loss function. Gradient descent is an optimization algorithm that is used to train complex machine learning and deep learning models. The cost function within gradient descent measures the acc...
The FAST-R-CNN technique is first used to detect targets. As the basic framework of target detection, FAST R-CNN combines deep learning technology to improve the accuracy of the target, and enhances the robustness to complex scenes through deep feature learning. Using RoI pooling to extract ...
In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the ...
[29] implemented the prediction of an optimal topological configuration of the structure under arbitrary load and volumetric constraints in the framework of cGAN [30]. Herath et al. [31] proposed a novel deep learning-based accelerated topology optimization technique that combines conditional ...
Adam is being adapted for benchmarks in deep learning papers. For example, it was used in the paper “Show, Attend and Tell: Neural Image Caption Generation with Visual Attention” on attention in image captioning and “DRAW: A Recurrent Neural Network For Image Generation” on image generatio...
( 1 ). specifically, we enable two key improvements in the riemannian space, including (1) to approximate the riemannian gradient and hessian using the subsampling technique and (2) to improve the subproblem formulation by replacing the trust-region constraint with a cubic regularization term. the...