In this work, we propose a very deep neural network comprised of 16 Convolutional layers compressed with the Fire Module adapted from the SQUEEZENET model. We ...
May 5, 2017 · In this work, we propose a very deep neural network comprised of 16 Convolutional layers compressed with the Fire Module adapted from the ...
This work proposes a very deep neural network comprised of 16 Convolutional layers compressed with the Fire Module adapted from the SQUEEZENET model, ...
In this paper, we address the issue of speed and size by proposing a compressed convolutional neural network model namely Residual Squeeze VGG16.
In this paper, we address the issue of speed and size by proposing a compressed convolutional neural network model namely Residual Squeeze VGG16. Proposed model ...
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Our research primarily focuses on analyzing cervical cell images by leveraging deep neural networks to develop object detection models.
Missing: Squeeze | Show results with:Squeeze
The proposed ReSTiNet is a novel compressed convolutional neural network that addresses the issues of size, detection speed, and accuracy.
Missing: Squeeze | Show results with:Squeeze
Oct 6, 2021 · The VGG16 model is used in several deep learning image classification problems, but smaller network architectures such as GoogLeNet and ...
Missing: Squeeze | Show results with:Squeeze
This study is performed to analyze the use of VGG16 in providing and improving the road extraction from remote sensing images (RSIs).
We also assess the effect of SE blocks when operating on non-residual networks by conducting experiments with the. VGG-16 [11] and BN-Inception architecture [6] ...