Here, we will use Long Short-Term Memory (LSTM) neural network cells in our autoencoder model. LSTM networks are a sub-type of the more general recurrent neural networks (RNN). A key attribute of ...

This repository presents an implementation of a Variational Autoencoder (VAE) tailored for the generation ... and the computation of advanced loss metrics. Additionally, the model facilitates the ...

In autoencoders, the image must be unrolled into a single vector and the network must be built following the constraint on the number of inputs. The block diagram of a Convolutional Autoencoder is ...

Abstract: As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In ...

Abstract: This work addresses the challenge of transferability of autoencoder models for lossy compression of ... We show the high transferability and generalizability of our A1D-CAE model for ...

SHENZHEN, China, Feb. 14, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, they Announced the ...