Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset.
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Updated
Mar 5, 2021 - C++
Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset.
Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset
Comparison of multiple methods for calculating MNIST hand-written digits similarity.
3 part project: A. bottleneck autoencoder, B. manhattan distance, C. earth mover's distance
Reducing MNIST image data dimensionality by extracting the latent space representations of an Autoencoder model. Comparing these latent space representations to the default MNIST representation
Fast EMD for Python: a wrapper for Pele and Werman's C++ implementation of the Earth Mover's Distance metric
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