Sission / Coupled-VAE-Improved-Robustness-and-Accuracy-of-a-Variational-AutoencoderLinks
We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs the outliers with a higher penalty by generalizing the original loss function to the coupled entropy funct…
☆19Updated 4 years ago
Alternatives and similar repositories for Coupled-VAE-Improved-Robustness-and-Accuracy-of-a-Variational-Autoencoder
Users that are interested in Coupled-VAE-Improved-Robustness-and-Accuracy-of-a-Variational-Autoencoder are comparing it to the libraries listed below
Sorting:
- A Pytorch implementation of the paper `Deep Autoencoding Gaussian Mixture Model For Unsupervised Anomaly Detection` by Zong et al.☆69Updated 5 years ago
- TensorFlow implementation for the GP-VAE model described in https://arxiv.org/abs/1907.04155☆143Updated 2 years ago
- Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection☆65Updated 5 years ago
- A curated list of time series augmentation resources.☆65Updated 4 years ago
- Kernel Change-point Detection with Auxiliary Deep Generative Models (ICLR 2019 paper)☆62Updated 2 years ago
- PyTorch implementation of SDAE (Stacked Denoising AutoEncoder)☆127Updated 5 years ago
- An encoder-decoder framework for learning from incomplete data☆45Updated 2 years ago
- ☆144Updated 6 months ago
- Adversarial Attacks on Deep Neural Networks for Time Series Classification☆80Updated 5 years ago
- Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance☆77Updated 6 years ago
- Code of the paper 'Neural Transformation Learning for Anomaly Detection' published in ICML 2021☆49Updated 3 years ago
- Contrastive Learning for Time Series☆40Updated 2 years ago
- Companion code for the self-supervised anomaly detection algorithm proposed in the paper "Detecting Anomalies within Time Series using Lo…☆17Updated 4 years ago
- Variational Adversarial Deep Domain Adaptation implementation (TensorFlow 1.x)☆11Updated 7 years ago
- Variational autoencoder for anomaly detection (in PyTorch).☆47Updated 6 years ago
- Implementation of 'Self-Adversarial Variational Autoencoder with Gaussian Anomaly Prior Distribution for Anomaly Detection'☆21Updated 4 years ago
- My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection☆421Updated 3 years ago
- Domain Adaptation for Time Series Under Feature and Label Shifts☆130Updated 2 years ago
- This repository is the PyTorch implementation of GAN Ensemble for Anomaly Detection.☆40Updated 4 years ago
- Offical implementation of "Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series" (ICLR 2022)☆161Updated 3 years ago
- Implementation of the MIWAE method for deep generative modelling of incomplete data sets.☆41Updated last year
- Multi-Scale Convolutional Neural Network for Time Series Classification (MCNN)☆25Updated 6 years ago
- ☆100Updated 7 years ago
- Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates☆116Updated 5 years ago
- Adversarial autoencoder (basic/semi-supervised/supervised)☆29Updated 3 years ago
- Official implementation of "Classification-Based Anomaly Detection for General Data" by Liron Bergman and Yedid Hoshen, ICLR 2020.☆90Updated last year
- [SDM 2022] Towards Similarity-Aware Time-Series Classification☆85Updated 2 years ago
- Pytorch implementation of SOM-VAE: INTERPRETABLE DISCRETE REPRESENTATION LEARNING ON TIME SERIES https://arxiv.org/pdf/1806.02199v7.pdf☆33Updated 6 years ago
- Generative Adversarial Imputation Networks (GAIN) Pytorch version☆29Updated 7 years ago
- This repository contains the codes to reproduce the results of our proposed novelty detection algorithm based on adversarially robust aut…☆19Updated 2 years ago