Compression of models and data in deep learning

Thesis

We face many challenges in deploying high-performance neural networks in practice. These challenges are predominantly due to the size of neural networks and apply to both training and inference. Compressing neural networks to make them train and run more efficiently is therefore crucial and has been a parallel line of research from the early days of neural networks development. The two main compression techniques in deep learning, which are the focus of this thesis, are pruning and quantizati...

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Authors: Alizadeh, M

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: image compression; neural network quantization; deep learning; data compression; neural network pruning; quantization
• Subjects: Image compression; Neural networks (Computer science); Deep learning (Machine learning); Data compression (Computer science); Data compression (Telecommunication)