The core of Autoencoder is the code-decode operation. Both the encoder and decoder may be Convolutional Neural Network or fully-connected feedforward neural networks. An autoencoder has three main ...

Convolutional Autoencoder is a variant of Convolutional Neural Networks that are used as the tools for unsupervised learning of convolution filters. They are generally applied in the task of image ...

Convolutional. Convolutional autoencoders encode input data by splitting the data up into subsections and then converting these subsections into simple signals that are summed together to create a new ...

This block can seamlessly be integrated into an existing autoencoder architecture to facilitate SU analysis. 2.4 Mathematical foundations of the autoencoder. In order to perform the endmember ...

The convolutional autoencoder comprises an encoder and a decoder. The encoder consists of three convolutional layers, each followed by a ReLU activation function and a max-pooling layer. The decoder ...

First, 1-D convolutional autoencoder is proposed in 1-DRCAE for feature extraction. Second, a deconvolution operation is developed as decoder of 1-DRCAE to reconstruct the filtered signals. Third, ...

This article presents a conditional convolutional autoencoder-based monitoring method, which is of twofold, for identifying wind turbine blade breakages. First, a novel conditional convolutional ...

Convolutional Autoencoder. Convolutional Autoencoder is a variant of Convolutional Neural Networks that are used as the tools for unsupervised learning of convolution filters. They are generally ...