Vae Anomaly Detector
Experiments on unsupervised anomaly detection using variational autoencoder. The variational autoencoder is implemented in Pytorch.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Deeplearning Models | 16,142 | 3 months ago | 4 | mit | Jupyter Notebook | |||||
| A collection of various deep learning architectures, models, and tips | ||||||||||
| Pytorch Tutorial | 7,372 | a year ago | 28 | mit | Jupyter Notebook | |||||
| Build your neural network easy and fast, 莫烦Python中文教学 | ||||||||||
| Alae | 2,850 | 3 years ago | 31 | Python | ||||||
| [CVPR2020] Adversarial Latent Autoencoders | ||||||||||
| Deeplearning_tutorials | 1,216 | 5 years ago | 7 | Jupyter Notebook | ||||||
| The deeplearning algorithms implemented by tensorflow | ||||||||||
| Deep Learning Experiments | 1,050 | 4 months ago | 10 | mit | Jupyter Notebook | |||||
| Videos, notes and experiments to understand deep learning | ||||||||||
| Deepsvg | 829 | 3 months ago | 28 | mit | Jupyter Notebook | |||||
| [NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data. | ||||||||||
| Atlasnet | 561 | 2 years ago | 11 | mit | Python | |||||
| This repository contains the source codes for the paper "AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation ". The network is able to synthesize a mesh (point cloud + connectivity) from a low-resolution point cloud, or from an image. | ||||||||||
| Torchlayers | 547 | 2 years ago | 4 | mit | Python | |||||
| Shape and dimension inference (Keras-like) for PyTorch layers and neural networks | ||||||||||
| Vae Cvae Mnist | 508 | 5 months ago | 1 | Python | ||||||
| Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch | ||||||||||
| Generative Models | 414 | 5 years ago | 1 | mit | Jupyter Notebook | |||||
| Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN | ||||||||||
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