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

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

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
01_Autoencoder_Classifier_End_to_End_Training.ipynb		01_Autoencoder_Classifier_End_to_End_Training.ipynb
02_Autoencoder_Classifier_Layer_Wise_Pre_training_Fashion_MNIST.ipynb		02_Autoencoder_Classifier_Layer_Wise_Pre_training_Fashion_MNIST.ipynb
03_Retinal_Vessel_Detection_Autoencoder_Segmentation.ipynb		03_Retinal_Vessel_Detection_Autoencoder_Segmentation.ipynb
04_Sparse_Autoencoder_MNIST_Classification.ipynb		04_Sparse_Autoencoder_MNIST_Classification.ipynb
05_Denoising_Autoencoder_MNIST_Classification.ipynb		05_Denoising_Autoencoder_MNIST_Classification.ipynb
Autoencoder_CIFAR10,_Denoising_MNIST.ipynb		Autoencoder_CIFAR10,_Denoising_MNIST.ipynb
LICENSE		LICENSE
README.md		README.md