Alternatives and similar repositories for RobustAutoencoder

Users that are interested in RobustAutoencoder are comparing it to the libraries listed below

• danieltan07 / dagmm
  My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
  ☆416Updated 2 years ago
• leibinghe / GAAL-based-outlier-detection
  GAAL-based Outlier Detection
  ☆134Updated 3 years ago
• lukasruff / Deep-SVDD
  Repository for the Deep One-Class Classification ICML 2018 paper
  ☆257Updated 6 years ago
• houssamzenati / Adversarially-Learned-Anomaly-Detection
  ALAD (Proceedings of IEEE ICDM 2018) official code
  ☆137Updated 6 years ago
• tnakae / DAGMM
  DAGMM Tensorflow implementation
  ☆180Updated 2 years ago
• houssamzenati / Efficient-GAN-Anomaly-Detection
  ☆439Updated 4 years ago
• raghavchalapathy / oc-nn
  Repository for the One class neural networks paper
  ☆166Updated 6 years ago
• RomainSabathe / dagmm
  A Pytorch implementation of the paper `Deep Autoencoding Gaussian Mixture Model For Unsupervised Anomaly Detection` by Zong et al.
  ☆70Updated 5 years ago
• Newcomer520 / tf-dagmm
  Tensorflow Implementation of dagmm: Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection, Zong et al, 2018
  ☆57Updated 7 years ago
• tungk / OED
  Outlier Detection for Time Series with Recurrent Autoencoder Ensembles
  ☆74Updated 6 years ago
• danielgy / Paper-list-on-Imbalanced-Time-series-Classification-with-Deep-Learning
  ☆188Updated 7 years ago
• twairball / keras_lstm_vae
  Keras implementation of LSTM Variational Autoencoder
  ☆228Updated 6 years ago
• JGuymont / vae-anomaly-detector
  Experiments on unsupervised anomaly detection using variational autoencoder. The variational autoencoder is implemented in Pytorch.
  ☆65Updated 2 years ago
• akash13singh / lstm_anomaly_thesis
  Anomaly detection for temporal data using LSTMs
  ☆225Updated 4 years ago
• raghavchalapathy / Deep-Learning-for-Anomaly-Detection-A-Survey
  This paper is continuously updated with deep anomaly detection methods and their applications
  ☆104Updated 6 years ago
• alimirzaei / adverserial-autoencoder-keras
  Keras Implementation of adverserial autoencoder (AAE)
  ☆55Updated 6 years ago
• otenim / AnomalyDetectionUsingAutoencoder
  Anomaly detection using Autoencoder implemented with Keras 2.
  ☆68Updated 6 years ago
• binli826 / LSTM-Autoencoders
  Anomaly detection for streaming data using autoencoders
  ☆201Updated 3 years ago
• ZhouYuxuanYX / Unsupervised-Deep-Learning-Framework-for-Anomaly-Detection-in-Time-Series-
  Unsupervised deep learning framework with online(MLP: prediction-based, 1 D Conv and VAE: reconstruction-based, Wavenet: prediction-based…
  ☆126Updated 2 years ago
• LiDan456 / GAN-AD
  We used generative adversarial networks (GANs) to do anomaly detection for time series data.
  ☆150Updated 6 years ago
• ldeecke / vae-torch
  Variational autoencoder for anomaly detection (in PyTorch).
  ☆48Updated 6 years ago
• chen0040 / keras-anomaly-detection
  Anomaly detection implemented in Keras
  ☆376Updated 7 years ago
• izikgo / AnomalyDetectionTransformations
  A simple and effective method for single-class classification of images
  ☆161Updated 3 years ago
• GuansongPang / deviation-network
  Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot …
  ☆153Updated 4 years ago
• LeeDoYup / AnoGAN-tf
  Unofficial Tensorflow Implementation of AnoGAN (Anomaly GAN)
  ☆280Updated 6 years ago
• y-bar / ml-based-anomaly-detection
  Spectral Residual
  ☆79Updated 2 years ago
• aurotripathy / lstm-anomaly-detect
  Example code for neural-network-based anomaly detection of time-series data (uses LSTM)
  ☆183Updated 8 years ago
• raghavchalapathy / rcae
  contains the code for models in the paper Robust, Deep and Inductive Anomaly Detection
  ☆35Updated 8 years ago
• nicococo / ClusterSvdd
  Cluster Support Vector Data Description
  ☆20Updated 8 years ago
• lukasruff / Deep-SAD-PyTorch
  A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.
  ☆347Updated 2 years ago