266 Open source projects that are alternatives of or similar to Deep Sad Pytorch

A log analysis toolkit for automated anomaly detection [ISSRE'16]

A robust, and flexible open source User & Entity Behavior Analytics (UEBA) framework used for Security Analytics. Developed with luv by Data Scientists & Security Analysts from the Cyber Security Industry. [PRE-ALPHA]

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

Implementation of Visual Feature Attribution using Wasserstein GANs (VAGANs, https://arxiv.org/abs/1711.08998) in PyTorch

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Abnormal Event Detection in Videos using SpatioTemporal AutoEncoder

A toolkit for automated log parsing [ICSE'19, TDSC'18, DSN'16]

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

A Python toolkit for rule-based/unsupervised anomaly detection in time series

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)

Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

Kibana Alert & Report App for Elasticsearch

Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge

A large collection of system log datasets for AI-powered log analytics

Semi-supervised Medical Image Segmentation through Dual-task Consistency

📜 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

Pytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)

Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition

Protein-compound affinity prediction through unified RNN-CNN

WinDBG Anti-RootKit Extension

A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

An Integrated Experimental Platform for time series data anomaly detection.

Code for paper "Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation"

Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"

Deep Learning sample programs using PyTorch in C++

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

Latent space autoregression for novelty detection.

Anomaly detection implemented in Keras

A state-of-the-art semi-supervised method for image recognition

A PyTorch implementation of the Deep SVDD anomaly detection method

Code for Adversarial Training Methods for Semi-Supervised Text Classification

Anomaly detection

PyTorch implementation of Adversarially Learned Inference (BiGAN).

An open-source, low-code machine learning library in Python

BERT, AWS RDS, AWS Forecast, EMR Spark Cluster, Hive, Serverless, Google Assistant + Raspberry Pi, Infrared, Google Cloud Platform Natural Language, Anomaly detection, Tensorflow, Mathematics

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019

Machine Learning Lectures at the European Space Agency (ESA) in 2018

Code for reproducing ICT ( published in IJCAI 2019)

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

This is a times series anomaly detection algorithm, implemented in Python, for catching multiple anomalies. It uses a moving average with an extreme student deviate (ESD) test to detect anomalous points.

Tidy anomaly detection

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

😔 😞 😣 😖 😩 Detect depression on social media using the ssToT method introduced in our ASONAM 2017 paper titled "Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media"

Merlion: A Machine Learning Framework for Time Series Intelligence

Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]

SyntheticSun is a defense-in-depth security automation and monitoring framework which utilizes threat intelligence, machine learning, managed AWS security services and, serverless technologies to continuously prevent, detect and respond to threats.

An Unsupervised Graph-based Toolbox for Fraud Detection

A full pipeline AutoML tool for tabular data

Implementation with some extensions of the paper "Snowball: Extracting Relations from Large Plain-Text Collections" (Agichtein and Gravano, 2000)

Healthbot configuration examples. Scripts to manage Healthbot. Closed loop automation. Healthbot building blocks description and troubleshooting guide

PCA and DBSCAN based anomaly and outlier detection method for time series data.

Anomaly detection for streaming time series, featuring automated model selection.

Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)

An End-to-end Outlier Detection System

Log-based Impactful Problem Identification using Machine Learning [FSE'18]

A curated list of awesome anomaly detection resources