├── 01_Autoencoder_Classifier_End_to_End_Training.ipynb ├── 02_Autoencoder_Classifier_Layer_Wise_Pre_training_Fashion_MNIST.ipynb ├── 03_Retinal_Vessel_Detection_Autoencoder_Segmentation.ipynb ├── 04_Sparse_Autoencoder_MNIST_Classification.ipynb ├── 05_Denoising_Autoencoder_MNIST_Classification.ipynb ├── Autoencoder_CIFAR10,_Denoising_MNIST.ipynb ├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Abhishek Singh Sambyal 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Autoencoders-using-Pytorch 2 | 3 | In this project, nuances of the autoencoder training were looked over. 4 | 1. Autoencoder end-to-end training for classifying MNIST dataset. [[Notebook01](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/01_Autoencoder_Classifier_End_to_End_Training.ipynb)] 5 | 2. Autoencoder Layer Wise Pre-training (Stacking) for Fashion-MNIST. [[Notebook02](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/02_Autoencoder_Classifier_Layer_Wise_Pre_training_Fashion_MNIST.ipynb)] 6 | 3. [DRIVE (Digital Retinal Images for Vessel Extractions)](https://www.isi.uu.nl/Research/Databases/DRIVE/) dataset patchwise segmentation using Autoencoder. [[Notebook03](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/03_Retinal_Vessel_Detection_Autoencoder_Segmentation.ipynb)] 7 | 4. Sparse Denoising Autoencoder (SDAE) for classification of MNIST dataset. [[Notebook04](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/04_Sparse_Autoencoder_MNIST_Classification.ipynb), [Notebook05](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/05_Denoising_Autoencoder_MNIST_Classification.ipynb)] 8 | 9 | ## Built With 10 | 11 | * [Pytorch](https://pytorch.org/) framework 12 | 13 | ## License 14 | This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/LICENSE) file for details 15 | --------------------------------------------------------------------------------