The most basic architecture of an autoencoder is a feed-forward architecture, with a structure much like a single layer perceptron used in multilayer perceptrons. Much like regular feed-forward neural ...

Person Segmentation using custom Autoencoder architecture and evaluation using IoU and Dice metrics, will also include Unet architecture in the future. autoencoder-architecture person-segmentation.

We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we introduce a novel deep network architecture using a hierarchical ...

In this article, we will cover a simple Long Short Term Memory autoencoder with the help of Keras and python. What is an LSTM autoencoder? LSTM autoencoder is an encoder that makes use of LSTM encoder ...

Autoencoder architecture and loss function #240. Copy link. Description. Arksyd96. opened on Apr 16, 2023. Issue body actions. Hello everyone, ... Firstly, in the compvis version of the autoencoder, ...

This paper proposes a deep learning architecture using Bi-directional Long Short Term Memory and Autoencoder for stance prediction. We illustrate, through empirical studies, that the method is ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...