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.

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

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

Spectrum encoder and decoder

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.

Framework for the AE reconstruction and feature based AD

Source separation of underwater acoustic radiated noise signals from ships with unknown numbers of signals. Using keras 2.2.4 with tensorflow-gpu 1.12.0 backend.

KAE : KAN-based AutoEncoder (AE, VAE, VQ-VAE, RVQ, etc.)