autoencoder projects - colorisation, denoising | tensorflow
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Updated
Oct 15, 2022 - Jupyter Notebook
autoencoder projects - colorisation, denoising | tensorflow
Includes code to Generative models like Variational Autoencoders,Generative Adversarial Networks
Keras implementation of Deep Learning Models applied to the MNIST and Polynomial datasets. Repository for the Software and Computing for Nuclear and Subnuclear Physics Project.
Simple implementation of Autoencoder with mxnet and scala.
Code and demos - XVII Avogadro Meeting (2021)
Pythorch implementation of Winner-Take-All Autoencoder
Autoencoders are a type of neural network used for unsupervised learning. In unsupervised learning, the model learns patterns from the data without using labeled outcomes. The goal is to find the underlying structure or representation of the data.
Different models of autoencoders: shallow, deep, convolutional, VAE, IWAE, DVAE, DIWAE
AutoEncoder on MNIST Digit
Implementation of ML algorithms and concepts from scratch and using scikit learn.
Comparative-Models for MNIST Dataset
UB Computer Vision
Autoencoder implementations and experiments with MNIST. MSU DL course.
Keras and Pytorch implementation of autoencoders.
Convolutional Autotencoder for noise removal and fine tuned vgg16 model to classify denoised images.
Extracting features using PCA, DCT, Centroid features and Auto encoder of 1 hidden-layer then classifying using K-means, GMM, SVM
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