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Variational AutoEncoders (VAE) with PyTorch - Alexander Van de Kleut

avandekleut.github.io · 3,091 words · saved by 1 readers

Imagine that we have a large, high-dimensional dataset. For example, imagine we have a dataset consisting of thousands of images. Each image is made up of hundreds of pixels, so each data point has hundreds of dimensions. The manifold hypothesis states that real-world high-dimensional data actually consists of low-dimensional data that is embedded in the high-dimensional space. This means that, while the actual data itself might have hundreds of dimensions, the underlying structure of the data can be sufficiently described using only a few dimensions. This is the motivation behind dimensionality reduction techniques, which try to take high-dimensional data and project it onto a lower-dimensional surface. For humans who visualize most things in 2D (or sometimes 3D), this usually means projecting the data onto a 2D surface. Examples of dimensionality reduction techniques include principal component analysis (PCA) and t-SNE. Chris Olah’s blog has a great post reviewing some dimensionality

Download the jupyter notebook and run this blog post yourself! Motivation Imagine that we have a large, high-dimensional dataset. For example, imagine we have a dataset consisting of thousands of images. Each image is made up of hundreds of pixels, so each data point has hundreds of dimensions. The manifold hypothesis states that real-world high-dimensional data actually consists of low-dimensional data that is embedded in the high-dimensional space. This means that, while the actual data itself might have hundreds of dimensions, the underlying structure of the data can be sufficiently describ

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