A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
-
Updated
Jun 4, 2024 - Python
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
A set of Python scripts to develop and experiment with CC-based and CC-independent techniques combining PM-based feature extraction and dimensionality reduction
MACE = Machine (learning) Approach to Chemistry Emulation
An autoencoder model to extract features from images and obtain their compressed vector representation, inspired by the convolutional VVG16 architecture
Image Debanding using Inversion by Direct Iteration
Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various encoder-decoder configurations to optimize performance metrics like MSE, SSIM, and PSNR, aiming to achieve near-lossless data compression.
Comparing latent space representations using autoencoders and vision transformers using fMRI data.
VA-AM (Various Advanced - Analogue Methods) is a Python package based on the deep learning enhancement of the classical statistical Analogue Method. It provides several tools to analyse climatological extreme events, particularly heat waves.
Contains Deep Learning Content and Algorithm. ANN_CNN_RNN(LSTM-GRU)_AUTOENCODER
LOFAR System Health Management
This repository offers a TensorFlow-based anomaly detection system for cell images using adversarial autoencoders, capable of identifying anomalies even in contaminated datasets. Check out our code, pretrained models, and papers for more details.
A simple transformer-based autoencoder model
Short text clustering methods through differents approaches
PyTorch implementation of Self-training approch for short text clustering
ResNet-style Autoencoders: Implementing and training AEs, VAEs, and CVAEs on provided dataset with TSNE visualizations.
repo for practicing DL/genAI
Variational Auto Encoder
Beta Machine Learning Toolkit
This code loads network data, preprocesses it, reduces dimensions with an autoencoder, and trains multiple classifiers (KNN, RF, LR, SVM) for anomaly detection.
Add a description, image, and links to the autoencoder topic page so that developers can more easily learn about it.
To associate your repository with the autoencoder topic, visit your repo's landing page and select "manage topics."