Autoencoders use neural networks for unsupervised data compression. By forcing a bottleneck in hidden layers, they learn compact data representations. While initially used for compression, their value lies in using the encoder for transfer learning or the decoder to generate new data samples resembling the training data. Variational autoencoders improve this by ensuring the learned representation is close to a normal distribution, enabling reliable random sampling and generating realistic data. This segment explains the core concept of autoencoders – neural networks trained to reproduce their input, forcing them to learn a compressed representation in their hidden layers. The example of compressing a 200x200 pixel image to a 1000-neuron hidden layer illustrates how autoencoders achieve data compression by learning efficient representations. This process is explained in detail, highlighting the potential for data compression and the role of hidden layers in representing the input data. This segment introduces variational autoencoders (VAEs), an improvement over basic autoencoders. VAEs address the limitations of basic autoencoders by using a mean and variance vector to represent the data's encoding, allowing for sampling from the learned distribution. The addition of a divergence loss ensures that the encoder's output remains close to a normal distribution, enabling the generation of realistic samples from the learned distribution. The segment explains how this modification allows for generating random data samples that resemble the training data.
