Variational autoencoders for supervision, calibration and multimodal learning - Joy, TW (2022)
Learning representations of data has long been a desirable goal in machine learning. Constructing such representations enables downstream tasks such as classification or object detection to be preformed efficiently. Furthermore, it is desirable to have these representations be constructed in such a way so they are interpretable, which allows for fine grained intervention and reasoning on characteristics of the input. Other tasks may include, cross-generation between modalities, or calibrating predictions such that their confidence matches their accuracy. An effective way to learn representations is through a Variational Autoencoder (VAE), which performs variational inference on the latent variables of the observable input. In this thesiswe show how the VAE, can be utilised to: incorporate label information into the learning process; learn shared-representations of multimodal data; and calibrate predictions of existing neural classifiers.