All Projects → NetManAIOps → Bagel

NetManAIOps / Bagel

Licence: other
IPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Bagel

Pytorch Vae
A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch
Stars: ✭ 181 (+302.22%)
Mutual labels:  vae, variational-autoencoder
Variational-Autoencoder-pytorch
Implementation of a convolutional Variational-Autoencoder model in pytorch.
Stars: ✭ 65 (+44.44%)
Mutual labels:  vae, variational-autoencoder
S Vae Tf
Tensorflow implementation of Hyperspherical Variational Auto-Encoders
Stars: ✭ 198 (+340%)
Mutual labels:  vae, variational-autoencoder
Vae Tensorflow
A Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).
Stars: ✭ 117 (+160%)
Mutual labels:  vae, variational-autoencoder
soft-intro-vae-pytorch
[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"
Stars: ✭ 170 (+277.78%)
Mutual labels:  vae, variational-autoencoder
Deep Learning With Python
Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.
Stars: ✭ 134 (+197.78%)
Mutual labels:  vae, variational-autoencoder
Variational Recurrent Autoencoder Tensorflow
A tensorflow implementation of "Generating Sentences from a Continuous Space"
Stars: ✭ 228 (+406.67%)
Mutual labels:  vae, variational-autoencoder
Python World
Stars: ✭ 98 (+117.78%)
Mutual labels:  vae, variational-autoencoder
MIDI-VAE
No description or website provided.
Stars: ✭ 56 (+24.44%)
Mutual labels:  vae, variational-autoencoder
Video prediction
Stochastic Adversarial Video Prediction
Stars: ✭ 247 (+448.89%)
Mutual labels:  vae, variational-autoencoder
Pytorch cpp
Deep Learning sample programs using PyTorch in C++
Stars: ✭ 114 (+153.33%)
Mutual labels:  vae, anomaly-detection
vae-concrete
Keras implementation of a Variational Auto Encoder with a Concrete Latent Distribution
Stars: ✭ 51 (+13.33%)
Mutual labels:  vae, variational-autoencoder
Mojitalk
Code for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090
Stars: ✭ 107 (+137.78%)
Mutual labels:  vae, variational-autoencoder
Tensorflow Mnist Cvae
Tensorflow implementation of conditional variational auto-encoder for MNIST
Stars: ✭ 139 (+208.89%)
Mutual labels:  vae, variational-autoencoder
Smrt
Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
Stars: ✭ 102 (+126.67%)
Mutual labels:  vae, variational-autoencoder
Cada Vae Pytorch
Official implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)
Stars: ✭ 198 (+340%)
Mutual labels:  vae, variational-autoencoder
Vae protein function
Protein function prediction using a variational autoencoder
Stars: ✭ 57 (+26.67%)
Mutual labels:  vae, variational-autoencoder
Vae For Image Generation
Implemented Variational Autoencoder generative model in Keras for image generation and its latent space visualization on MNIST and CIFAR10 datasets
Stars: ✭ 87 (+93.33%)
Mutual labels:  vae, variational-autoencoder
Vae Cvae Mnist
Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
Stars: ✭ 229 (+408.89%)
Mutual labels:  vae, variational-autoencoder
benchmark VAE
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Stars: ✭ 1,211 (+2591.11%)
Mutual labels:  vae, variational-autoencoder

Bagel

The implementation of 'Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder' Models are in model.py.

Dependencies

python >= 3.7

pip install -r requirements.txt

Run

python main.py

Citation

Li, Zeyan, Wenxiao Chen, and Dan Pei. "Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder." 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC). IEEE, 2018.

@inproceedings{li2018robust,
  title={Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder},
  author={Li, Zeyan and Chen, Wenxiao and Pei, Dan},
  booktitle={2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC)},
  pages={1--9},
  year={2018},
  organization={IEEE}
}
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].