Comparison of Selected Efficient Deep Learning Techniques for Convolutional Neural Networks in Classification - Phamová, Xuan Vi; Zamazal, Ondřej; Vacura, Miroslav; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
The deployment of convolutional neural networks (CNNs) on resource-constrained edge devices requires the application of model compression techniques to balance performance, size, and inference time. This thesis investigates three key techniques — weight pruning, quantization, and knowledge distillation — evaluating their impact on CNN performance across various datasets and model architectures. Experimental results show that weight pruning, when applied to simpler custom CNNs, significantly reduces model size while slightly improving accuracy. However, excessive pruning in more complex pretrained models led to a considerable drop in performance, highlighting the need for more advanced strategies in complex architectures. Quantization techniques, particularly Post-Training Quantization (PTQ), proved highly effective in reducing model size by 75-90% and accelerating inference time by up to 1.5-8×, without substantial accuracy degradation. In contrast, Quantization-Aware Training (QAT) offered similar benefits but introduced minor accuracy losses and the risk of overfitting, particularly when applied to larger models like InceptionV3. Knowledge distillation demonstrated strong potential for transferring knowledge from larger teacher models to smaller student models, resulting in improved classification accuracy without increasing model size or inference time. These techniques offer promising solutions for optimizing CNNs, ensuring efficient performance while maintaining competitive accuracy.
Vedoucí práce: Ondřej Zamazal
Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2025
Způsob přístupu: Internet
Keywords: data analytics [obor bakal. práce]; bakalářské práce; bachelor's theses