Variational autoencoders for supervision, calibration and multimodal learning

Thesis

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 calibrat...

Expand abstract

Authors: Joy, TW

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: VAEs; semi-supervised learning; calibration; multi-model Learning
• Subjects: Machine Learning