Project of Paraphrase Identification Based on Weighted URAE, Unit Similarity and Context Correlation Feature
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Updated
Dec 7, 2022 - Python
Project of Paraphrase Identification Based on Weighted URAE, Unit Similarity and Context Correlation Feature
My personal attempt to write well designed autoencoders-NNets.
Experiments with Variational Autoencoders in pytorch
Base space-time model with on top a classic autoencoder to perform video anomaly detection
Novel Pooling for anti-aliasing convolutional neural networks
A Recommender System that predicts ratings from 1 to 5 on MovieLens 1M Dataset
Linear Regression, Logistic Regression, Neural Networks, Convolutional Neural networks, Auto Encoders
PyTorch implementation of different types of autoencoders
Comparison between a linear and convolutional autoencoder.
Thesis work on Video Anomaly Detection
Deep Learning codes
Convolutional Autoencoders for Anomaly Detection
This repository explores the cutting-edge field of anomaly detection using deep learning, particularly through the implementation of autoencoders. Our approach revolves around the concept of reconstruction error, with a specific focus on leveraging the Mean Absolute Error (MAE) as the determining factor for anomalies within complex datasets.
A python package made to streamline the usage of Variational Autoencoders, understand the algorithm first before using this package
Deep k-means (Autoencoder + k-means clustering)
Exploring the importance of image resolution on self-supervised learning methods for multispectral imagery
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