The Deep Learning Architect's Handbook

5

Understanding Autoencoders

Autoencoders are a type of model that was built mainly to accomplish representation learning. Representation learning is a type of deep learning task that focuses on generating a compact and representative feature to represent any single data sample, be it image, text, audio, video, or multimodal data. After going through some form of representation learning, a model will be able to map inputs into more representable features, which can be used to differentiate itself from other sample inputs. The representation obtained will exist in a latent space where different input samples will co-exist together. These representations are also known as embeddings. The applications of autoencoders will be tied closely to representation ...