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 learning applications, and some applications include generating predictive features for other subsequent supervised learning objectives, comparing and contrasting samples in the wild, and performing effective sample recognition.

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