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[bikhanal]

What is the difference between Autoencoder and Variational Autoencoder?

[bikhanal]

One of the limitations of a standard autoencoder is that it does not have a probabilistic interpretation, which can make it difficult to generate new data samples or to perform tasks such as anomaly detection. The output of a standard autoencoder is a deterministic reconstruction of the input data, which may not accurately capture the variability in the data.

To address this limitation, Variational Autoencoder (VAE) was introduced, which is a type of generative model that has a probabilistic interpretation. VAE is also based on neural networks and consists of two main parts: an encoder and a decoder, similar to a standard autoencoder.

However, instead of directly learning the encoding of input data, VAE learns a probability distribution over the encoding. Specifically, VAE learns to map the input data to a mean and variance vector, which is then used to sample from a distribution. The sampled vector is then fed into the decoder to generate a reconstructed output.

The advantage of using a probabilistic framework is that it allows the generation of new data samples by randomly sampling from the learned distribution over the encoding space. This means that VAE can learn a richer representation of the input data and generate new samples that are similar to the training data but not necessarily identical.

One of the key differences between VAE and a standard autoencoder is the use of a loss function that incorporates both the reconstruction error and a regularization term. The regularization term ensures that the learned distribution over the encoding space follows a predefined distribution, such as a Gaussian distribution. This helps to prevent overfitting and encourages the model to learn a smooth and continuous representation of the data.

[KhanalBijay]

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