Autoencoders Using Pytorch Medical Imaging Save
Medical Imaging, Denoising Autoencoder, Sparse Denoising Autoencoder (SDAE) End-to-end and Layer Wise Pretraining
Project README
Autoencoders-using-Pytorch
In this project, nuances of the autoencoder training were looked over.
- Autoencoder end-to-end training for classifying MNIST dataset. [Notebook01]
- Autoencoder Layer Wise Pre-training (Stacking) for Fashion-MNIST. [Notebook02]
- DRIVE (Digital Retinal Images for Vessel Extractions) dataset patchwise segmentation using Autoencoder. [Notebook03]
- Sparse Denoising Autoencoder (SDAE) for classification of MNIST dataset. [Notebook04, Notebook05]
Built With
- Pytorch framework
License
This project is licensed under the MIT License - see the LICENSE.md file for details
Open Source Agenda is not affiliated with "Autoencoders Using Pytorch Medical Imaging" Project. README Source: abhisheksambyal/Autoencoders-using-Pytorch-Medical-Imaging
Stars
41
Open Issues
0
Last Commit
5 years ago
License
Open Source Agenda Badge
