This image provides a complete visual guide to the autoencoder neural network, featuring five illustrations that detail each stage of the data encoding and decoding process. It serves as an ...

A autoencoder is a neural network that has three layers: an input layer, a hidden (encoding) layer, and a decoding layer. The network is trained to reconstruct its inputs, which forces the hidden ...

Similar to convolution neural networks, a convolutional autoencoder specializes in the learning of ... The Gaussian distribution is sampled to create a vector, which is fed into the decoding network, ...

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled ... training to recover the original undistorted input. The model learns a vector field for mapping ...

The result shows among the methods (support vector machine, neural network with dropout, autoencoder), neural network with added layers with dropout has the highest accuracy. And a comparison with the ...

In this paper, a novel local anomaly detection model DAGNN is proposed, which incorporates a graph neural network to better aggregate neighbors' distance information of each sample for forming its ...

In this work, we propose an autoencoder (AE) neural network (NN)-based reduced model to accelerate such simulations. The AE NN is first trained to find a low-dimensional latent representation of the ...

Therefore, in this article, we explored the combination of autoencoder and diffractive deep neural network, and proposed the AE-D2NN model. We adopted a knowledge distillation strategy, using a ...