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Variational Autoencoders
ariational autoencoders (VAEs) are a type of generative model that learns a probabilistic representation of data, allowing them to both encode and generate new data samples. They consist of an encoder network that maps input data to a latent space and a decoder network that reconstructs the original data from the latent representation. VAEs are particularly useful for tasks like dimensionality reduction, data generation, and anomaly detection.
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Variational Autoencoders
k.i. - Variational Autoencoders
Variational Autoencoders (VAEs) represent a significant advancement in generative models, merging deep learning principles with Bayesian inference principles. They are designed to efficiently encode high-dimensional data into a lower-dimensional latent space while facilitating the generation of new data points that mimic the underlying distribution of the training data.
At the core of a VAE is the autoencoder architecture, which consists of two primary components: the encoder and the decoder. The encoder maps input data to a latent representation (or latent variable), which captures the essential features of the data in a compressed form. The decoder, in turn, reconstructs the original data from this latent representation. However, unlike traditional autoencoders, VAEs incorporate probabilistic elements into their architecture, enabling them to learn a continuous probability distribution over the latent space.
The key innovation of VAEs is their use of variational inference. During training, the encoder outputs parameters of a probability distribution—typically a Gaussian distribution—rather than a fixed latent representation. This distribution encodes the uncertainty concerning the representation of the input data. The decoder is also probabilistic: it generates outputs based on samples drawn from this distribution. By doing so, the VAE can produce diverse outputs, sampling from the learned variation in data.
The training of VAEs involves maximizing the evidence lower bound (ELBO), which consists of two terms: the reconstruction loss and the Kullback-Leibler (KL) divergence. The reconstruction loss measures how closely the generated data matches the original input, encouraging accurate reconstructions. Conversely, the KL divergence term quantifies the difference between the learned latent distribution and a prior distribution—typically a standard normal distribution—thereby ensuring that the latent space is well-behaved and conducive to sampling.
This dual objective allows VAEs to achieve remarkable outcomes in generative tasks. They can generate high-quality images, interpolate between data points, and even perform data completion tasks. For instance, in the realm of image synthesis, VAEs can generate new images that are indistinguishable from real examples, while preserving the diversity inherent in the training dataset.
