Mar 15, 2018 · You need to have a single channel convolution layer with "sigmoid" activation to reconstruct the decoded image. Take a look at the example below. You can compile it with the …

Mar 1, 2020 · A new DNN model, one-dimensional convolutional auto-encoder (1D-CAE) is proposed for fault detection and diagnosis of multivariate processes in this paper. 1D-CAE is …

Jan 13, 2020 · In this article, a new DNN, one-dimensional residual convolutional autoencoder (1-DRCAE), is proposed for learning features from vibration signals directly in an unsupervised …

Mar 1, 2025 · Autoencoders aim to minimize reconstruction error which is the difference between the input and the reconstructed output. They use loss functions such as Mean Squared Error …

Apr 11, 2025 · Zhang and Qiu (2022) integrated the vector autoregressive model into the one-dimensional convolutional autoencoder, and proposed a dynamic-inner convolutional …

Apr 27, 2025 · Convolutional Neural Networks (CNNs) are well-known for their ability to process images by transforming a two-dimensional image into a compact, one-dimensional vector that …

Jun 5, 2017 · Try reshaping x_decoded_mean to your input shape since x_train[0:N,:] is shaped (1,784) but your output is (784,) something like. Thanks for contributing an answer to Stack …

This study proposes Sliding Window One-Dimensional Convolutional Autoencoder (SW1DCAE), an unsupervised vibration anomaly detection algorithm for industrial robots, that can directly …

Feb 24, 2024 · Autoencoders are a specific type of feedforward neural networks where the input is the same as the output. They compress the input into a lower-dimensional latent …

autoencoder. The above network uses the linear activation function and works for the case that the data lie on a linear surface. If the data lie on a nonlinear surface, it makes more sense to …