The Concept of Latent Space and Reconstruction

In a VAE, the hidden space is a lower-dimensional space where the important parts of the input data are shown using probabilities. This latent space is mapped to input data by the encoder, which makes a distribution (usually a Gaussian one) from which samples of latent variables are taken. This probability method lets the model take into account the data's doubt and variation. The decoder takes bits from this latent space and puts together the original data, making sure that the representation in the latent space keeps all the important data. This process not only makes it easier to compress and rebuild data, but it also makes it possible to create new data samples by taking samples from the latent space. This makes VAEs very useful for many tasks, including creating images, finding anomalies, and interpolating data.