The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.
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Updated
Nov 11, 2020 - Jupyter Notebook
The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.
Create a few popular Neural Networks from scratch using just Numpy
speech-enhancement-flask
This work applies autoencoder to denoise the image in the "cifar10" dataset.
The official implemenataion of the "Denoising Architecture for Unsupervised Anomaly Detection in Time-Series" paper.
Deep learning & neural network applications
BlurRemoval-Using-an-Autoencoder Are you poor at taking photos Just like me? Here I have made a Deep learning model using Autoencoder architecture to remove unwanted blur from the image.
All the models of Autoencoders that I've worked on
Heavy-Tailed distributions in Variational Autoencoder (VAE)
PyTorch implementations of an Undercomplete Autoencoder and a Denoising Autoencoder that learns a lower dimensional latent space representation of images from the MNIST dataset.
Semester 8 (Jan 2023 - May 2023)
The standard approach to image reconstruction using deep learning is to use clean image priors for training purposes. In this project, we attempt to achieve denoising without using a clean image prior and yet, achieving a performance comparable to, or sometimes, even better than that obtained using the conventional approach.
Autoencoder which will remove the noise from images. For training we need dataset with noise and dataset without nois, we dont havemnist data with noise so first we will add some gaussian noise into the whole mnist data.
Experimental Adversarial Attack notebooks on CV models
Using a Denoising Autoencoder (a neural network model) for classifying corrupted images.
Keras implementation of denoising convolution auto encoder on mnist
Implementation of de-noising auto-encoders in Keras using the Denoising Dirty Documents dataset on Kaggle.
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