Implementation of basic autoencoder architectures and SimCLRv2 - ligerfotis/representation_learning_tutorial ...

Convolutional Autoencoder using Keras and Tensorflow The repository contains some convenience objects and examples to build, train and evaluate a convolutional autoencoder using Keras. The used Keras ...

That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

The autoencoder (AE) is a fundamental deep learning approach to anomaly detection. AEs are trained on the assumption that abnormal inputs will produce higher reconstruction errors than normal ones. In ...

In this paper, we propose a lightweight autoencoder with hierarchical priors. The lightweight autoencoder reduces the model’s parameter size and calculation amount based on ensuring high fidelity and ...