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In what other cases do you think you could use a variational autoencoder?
Variational autoencoders (VAEs) are a deep learning technique for learning latent representations. They have also been used to draw images, achieve state-of-the-art results in semi-supervised learning, as well as interpolate between sentences.
Are variational Autoencoders better than autoencoders?
A variational autoencoder assumes that the source data has some sort of underlying probability distribution (such as Gaussian) and then attempts to find the parameters of the distribution. Implementing a variational autoencoder is much more challenging than implementing an autoencoder.
In the previous section we gave the following intuitive overview: VAEs are autoencoders that encode inputs as distributions instead of points and whose latent space “organisation” is regularised by constraining distributions returned by the encoder to be close to a standard Gaussian.
How is a variational autoencoder similar to a standard encoder?
Just as a standard autoencoder, a variational autoencoder is an architecture composed of both an encoder and a decoder and that is trained to minimise the reconstruction error between the encoded-decoded data and the initial data.
Which is an example of an autoencoder with its loss function?
Illustration of an autoencoder with its loss function. Let’s first suppose that both our encoder and decoder architectures have only one layer without non-linearity (linear autoencoder). Such encoder and decoder are then simple linear transformations that can be expressed as matrices.
How does autoencoder architecture create a bottleneck for data?
Thus, intuitively, the overall autoencoder architecture (encoder+decoder) creates a bottleneck for data that ensures only the main structured part of the information can go through and be reconstructed.