Creating Separable Encodings of Images

Learn about how images can be compressed into numerical vectors using neural networks.

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Let’s examine how neural networks can optimally compress information into numerical vectors. To do so, each element of the vector should encode distinct information from the others, a property we can achieve using a variational objective. This variational objective is the building block for creating VAE networks.

In the figure below, you can see an example of images from the CIFAR-10 datasetKrizhevsky, Alex. 2009. “CIFAR-10 and CIFAR-100 Datasets.” Toronto.edu. 2009. https://www.cs.toronto.edu/~kriz/cifar.html., along with an example of an early VAE algorithm that can generate fuzzy versions of these images based on a random number inputPiyush Malhotra, “Variational Autoencoders”, Autoencoder-Implementations. GitHub repository. 2018, https://www.piyushmalhotra.in/Autoencoder-Implementations/VAE/. More recent work on VAE networks has allowed these models to generate much better images.

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