Additionally, it conducts a performance comparison of Convolutional Neural Network (CNN) models in this context.Paulin, F.Lakshmi, P.Mapana Journal of Sciences
The concatenated feature is later reduced to the standard dimension using Principal Component Analysis (PCA) algorithm, generating the refined CNN feature. When applied in image classification and retrieval tasks, the PPC feature consistently outperforms the conventional CNN feature, regardless of the ...
CNNs have several layers, the most common of which are convolution, ReLu, and pooling. Layers in a convolutional neural network (CNN). Convolution layers act as filters—each layer applies a filter and extracts specific features from the image. These filter values are learned by the network wh...
A Stacking Algorithm for Convolution Neural Network 来自知网 作者X Zhang,Z Wang,L Liang,SO Electronic摘要 In order to improve the classification accuracy of convolution neural network,an improved Stacking algorithm...
"Unknown: Failed to get convolution algorithm. This is probably because cuDNN"错误通常与cuDNN库的卷积算法获取失败有关。在解决这个错误时,你需要注意cuDNN库的版本兼容性,确保正确安装和设置cuDNN库,以及更新GPU驱动程序。如果问题仍然存在,你可以尝试重新编译深度学习框架。希望本文对你解决该错误提供了一些帮助...
the algorithm consists of two parts: region proposal and object detection network. The region proposal mainly generates some object proposals with the saliency algorithm, and the object detection network carries out the detection function of the object based on Fast-RCNN. Our algorithm firstly process...
and interpreting data in a mechanism that imitates the human brain33,34,35. Deep learning can form an abstract high-level representation by combining low-level features to discover the rules of data. Therefore, in this paper, we use deep learning convolution neural network algorithm to extract ...
In this paper, we propose the algorithm for stroke lesion segmentation based on a deep convolutional neural network (CNN). The model is based on U-shaped CNN, which has been applied successfully to other medical image segmentation tasks. The network architecture was derived from the model present...
it necessitates extensive domain knowledge and signal processing techniques to extract appropriate features from raw data that align with a machine learning algorithm. DL methods, such as CNNs and Long Short-Term Memory Neural Networks (LSTMs), have demonstrated their effectiveness by automatically lea...
we first propose a traffic sign recognition algorithm based on compressive sensing domain and convolution neural networks for traffic sign recognition. The algorithm converts the image into a compressed sensing domain through the measurement matrix without reconstruction, and can extract the discriminant no...