Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

Anomaly detection is the process of finding items in a dataset that are different in some way from the majority of the items. For example, you could examine a dataset of credit card transactions to ...
Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

The Data Science Lab Autoencoder Anomaly Detection Using PyTorch Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a ...
GitHub

About An implement of AutoEncoder, Transformer and DDPM with cross-attention for Meteorlogical Graph seq-seq prediction

Feature extracting using basic Autoencoder with ConvNeXtV2 backbone or BYOL (not experimented) Implicit vector predicting using Transformer with both encoder and decoder Image generating using DDPM ...
IEEE

Learning Causal Representations Based on a GAE Embedded Autoencoder

Abstract: Traditional machine-learning approaches face limitations when confronted with insufficient data. Transfer learning addresses this by leveraging knowledge from closely related domains. The ...
GitHub

Autoencoder doc - add examples of using deepfeatures functionality

New issue New issue Open Open Autoencoder doc - add examples of using deepfeatures functionality #16637 Labels feature ...
Europe PMC

A tied-weight autoencoder for the linear dimensionality reduction of sample data

Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than ...
IEEE

Contrastive Semantic-Aware Masked Autoencoder for Point Cloud Self-Supervised Learning

Abstract: Masked Autoencoder (MAE) has shown remarkable potential in self-supervised representation learning for 3D point clouds. However, these methods primarily rely on point-level or low-level ...