pytorch>> Autoencoders-using-Pytorch-Medical-Imaging>> 返回
项目作者： abhisheksambyal

项目描述 ：
Medical Imaging, Denoising Autoencoder, Sparse Denoising Autoencoder (SDAE) End-to-end and Layer Wise Pretraining
高级语言： Jupyter Notebook
项目地址: git://github.com/abhisheksambyal/Autoencoders-using-Pytorch-Medical-Imaging.git


Autoencoders-using-Pytorch

In this project, nuances of the autoencoder training were looked over.

  1. Autoencoder end-to-end training for classifying MNIST dataset. [Notebook01]
  2. Autoencoder Layer Wise Pre-training (Stacking) for Fashion-MNIST. [Notebook02]
  3. DRIVE (Digital Retinal Images for Vessel Extractions) dataset patchwise segmentation using Autoencoder. [Notebook03]
  4. Sparse Denoising Autoencoder (SDAE) for classification of MNIST dataset. [Notebook04, Notebook05]

Built With

License

This project is licensed under the MIT License - see the LICENSE.md file for details