Efficiency in Deep Learning
Image and Video Deep Model Efficiency
Xin Liu (TU Delft - Pattern Recognition and Bioinformatics)
Marcel Reinders – Promotor (TU Delft - Pattern Recognition and Bioinformatics)
J.C. Gemert – Promotor (TU Delft - Pattern Recognition and Bioinformatics)
Silvia Pintea – Copromotor (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Deep learning is the core algorithmic tool for automatically processing large amounts of data. Deep learning models are defined as a stack of functions (called layers) with millions of parameters, that are updated during training by fitting them to data. Deep learning models have show remarkable accuracy gains on visual problems in video and images. Yet at the same time, this comes at a considerable computational cost that raises concerns about energy consumption. The escalation in the number of parameters and the surging demand for extensive data exacerbate these concerns. This thesis delves into the core of these concerns, proposing innovative techniques to enhance the efficiency of deep learning models. This thesis starts with exploring efficient deep learning models for video data, followed by efficient models for image data.....
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