Autoencoders-using-Pytorch-Medical-Imaging
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Medical Imaging, Denoising Autoencoder, Sparse Denoising Autoencoder (SDAE) End-to-end and Layer Wise Pretraining
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