🌐 Unmix hyperspectral data using the DMTS-Net model, integrating a dual-stream architecture to enhance spectral variability analysis and model performance.

📦 Integrate the tANS algorithm into ContikiNG for efficient data compression, reducing transmission size and energy use in IoT devices.

🖼️A Deep Learning project that implements Content-Based Image Retrieval (CBIR) functionality using a Convolutional Autoencoder on the CIFAR-10 dataset. The system learns compressed feature representations (embeddings) of images to clean noisy inputs and retrieve visually similar images efficiently.

This repo contains auto encoders and decoders using keras and tensor flow. It shows the exact encoding and decoding with the code part.

Cryptocurrency AI prediction model

Integrate your chemometric tools with the scikit-learn API 🧪 🤖

Official repository for "Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoders".

👋🚀 This project uses autoencoders to generate embeddings from datasets such as MNIST, Fashion-MNIST, CIFAR-10, and Glass Identification. The embeddings are reduced to two dimensions with manifold learning techniques to visualize and explore the effectiveness of combining representation learning with dimensionality reduction.

Source code and additional resources for paper "DupliMend: Online Detection and Refinement of Imprecise Activity Labels".

Single-Cell (Perturbation) Model Library

Anomaly Detection on Network Traffic Data

A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

A Deep Learning application powered by Streamlit and VAE to simulate electrocardiogram anomalies (LBBB, PVC, RBBB...) for data augmentation.

[ICCV 2023 Oral] Official Implementation of "Denoising Diffusion Autoencoders are Unified Self-supervised Learners"

Benchmarking Autoencoder, Isolation Forest, LOF, SVM, and Hybrid Models for Network Intrusion Detection on UNSW-NB15 with complete statistical validation and ML pipeline.

A compact pipeline for detecting emergency vehicles from audio using deep learning. Includes data download and preprocessing, MFCC/LFCC/Chroma feature extraction, model training (autoencoders for dimensionality reduction and FFNN/CNN/LSTM classifiers) and performance evaluation.

This project focuses on building an autoencoder-based anomaly detection model to learn latent patterns in malicious web traffic. The dataset provided contains only attack traffic, with no examples of normal user activity.

本项目复现了论文《Blind Unmixing Using Dispersion Model-Based Autoencoder to Address Spectral Variability》中的DMTS-Net模型，并在Jasper Ridge高光谱数据集上进行验证。

Project to detect errors in label printing using an autoencoder. Use the Roboflow file as both the training and test files: https://universe.roboflow.com/university-science-malaysia/label-printing-defect-version-2/dataset/25