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

Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

NeurIPS 2019: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

A machine learning plugin in Open Distro for Elasticsearch for real time anomaly detection on streaming data.

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

The standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.

Find big moving stocks before they move using machine learning and anomaly detection

List of tools & datasets for anomaly detection on time-series data.

Pytorch implementation of Conditional Image Synthesis with Auxiliary Classifier GANs

speech to text with self-supervised learning based on wav2vec 2.0 framework

Several SSL methods (Pi model, Mean Teacher) are implemented in pytorch

Pytorch implementation of "Graph Convolutional Networks for Text Classification"

log anomaly detection toolkit including DeepLog

A high-level machine learning and deep learning library for the PHP language.

Real-World Anomaly Detection in Surveillance Videos

Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"

Foremast adds application resiliency to Kubernetes by leveraging machine learnt patterns of application health to keep applications healthy and stable

Time series based anomaly detector

STUMPY is a powerful and scalable Python library for modern time series analysis

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

Source code for Skip-GANomaly paper

Repository for the Explainable Deep One-Class Classification paper

Unsupervised Data Augmentation (UDA)

SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

Implementation of Ladder Network in PyTorch.

Pytorch implementation of Virtual Adversarial Training

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]

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

Abnormal Event Detection in Videos using SpatioTemporal AutoEncoder

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

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

Kibana Alert & Report App for Elasticsearch

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

Semi-supervised Medical Image Segmentation through Dual-task Consistency

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

Protein-compound affinity prediction through unified RNN-CNN

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

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

Deep Learning sample programs using PyTorch in C++

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

Latent space autoregression for novelty detection.

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

Code for Adversarial Training Methods for Semi-Supervised Text Classification

PyTorch implementation of Adversarially Learned Inference (BiGAN).

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

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

Code for reproducing ICT ( published in IJCAI 2019)

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.

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

😔 😞 😣 😖 😩 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"

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

An Unsupervised Graph-based Toolbox for Fraud Detection

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

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

[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.

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

Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)

An End-to-end Outlier Detection System