Learning and interpreting deep representations from multi-modal data

Thesis

Deep learning has resulted in ground-breaking progress in a variety of domains, from core machine learning tasks such as image, language, and video understanding, to real-world industries such as medicine, autonomous driving, and agriculture. Its success has been driven by providing neural networks with manual supervision from large-scale labelled datasets such as ImageNet to automatically learn hierarchical data representations. However, obtaining large-scale labelled data is often a ve...

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Authors: Patrick, M

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: multi-modal learning; deep representations; interpretability; video understanding; self-supervised learning; clustering; explainable AI; machine learning; representation learning
• Subjects: Computer vision; Multi-modal learning; video understanding; Transfer learning (Machine learning); representation learning