Scalable continual deep learning with computational cost considerations

Thesis

Continual Learning (CL) is a emerging field that focuses on developing models capable of learning continuously from a incoming stream of data, as opposed to hundreds of passes over static, curated datasets. These models aim to retain previously acquired knowledge while seamlessly integrating new information, often with constraints like limited storage capacities. To advance this field, we first pinpoint the current research paradigm's limitations. We address these by (i) implementing more...

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Authors: Prabhu, AP

• DOI: 10.5287/ora-xq162om9j
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: slow-fast learning; data-centric; scalable; continual learning; kNN
• Subjects: Deep learning (Machine learning)