PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series

video summarization lstm-gan pytorch implementation

A Novel Approach leveraging Auto-Encoders, LSTM Networks and Maximum Entropy Principle for Video Super-Resolution (Upscaling and Frame Interpolation)

Sparse Residual LSTM Autoencoder | Robust Autoencoder for Anomaly Detection in ECG | 2024 대한전자공학회 추계학술대회 | Autumn Annual Conference of IEIE, 2024 | OMS 2

Time Series Forecasting using RNN, Anomaly Detection using LSTM Auto-Encoder and Compression using Convolutional Auto-Encoder

CobamasSensorOD is a framework used to create, train and visualize an autoencoder on sequential multivariate data.

The project is to show how to use LSTM autoencoder and Azure Machine Learning SDK to detect anomalies in time series data

High Volatility Stock Prediction using Long Short-term Memory (LSTM)

A complete workflow for building, training, and deploying a lightweight LSTM Autoencoder anomaly detector for temperature data on the ESP32 microcontroller—without TensorFlow or TFLite. This project uses PyTorch, ONNX, and C++ for efficient, real-time anomaly detection on edge devices.