The proposed solution uses an autoencoder to reconstruct the received packets and detect malicious packets based on the reconstruction error. A careful optimization of the model architecture allowed improving detection accuracy while reducing detection time. The proposed solution has been implemented on a...
I am very interested in training convolutional autoencoders in MATLAB 2019b. I have found the instruction trainAutoencoder, but it does not allow to specify the convolutional layers architecture. I want to design my autoencoder using Deep Network Designer tool, and then train it just as...
Use the defaultdeepSignalAnomalyDetectorarchitecture, which is a convolutional autoencoder. Set the window length to use the entire time window in both cases. First, set up the deep signal anomaly detector for the scattering sequences. Here the number of channels is equal to one ...
In this paper, it is tried to increase the encryption complexity and unpredictability of the encryption scheme using different phases of chaos game representation (GCR), logistic map diffusion, and convolutional auto-encoder-based image representation. In the proposed scheme, the original image's ...
The proposed network uses a Convolutional Auto-Encoder Neural Network (CANN) to extract and learn deep features of input images. Extracted deep features from each level are combined to make desirable features and improve results. To classify brain tumor into three categories (Meningioma, Glioma, ...
The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network
3.2. Dilated Convolutional Autoencoders The architecture of dilated convolutional autoencoders (DCAEs) is pretty similar to classical autoencoders [15]. Figure3shows the structure of a dilated convolutional autoencoder. The input is mapped into feature maps through an activation function: ...
We present a novel convolutional auto-encoder (CAE) for unsupervised feature learning. A stack of CAEs forms a convolutional neural network (CNN). Each CAE is trained using conventional on-line gradient descent without additional regularization terms. A max-pooling layer is essential to learn biolog...
e., a combined architecture of an auto-encoder and contrastive loss, outperforms a conventional convolutional neural network (CNN), as well as a convolutional auto-encoder (CAE) without using contrastive loss. Our final contrastive CAE ... S Liu,J Han,EL Puyal,... 被引量: 0发表: 2021年...
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started. - treper/DeepLearnToolbox