Memory Efficient Semantic Segmentation for Embedded Systems
Convolutional neural networks (CNNs) have made rapid progress in the last years and in fields, such as computer vision, they are considered state-of-the-art. However, CNNs are very computationally intensive. This makes them challenging to use in embedded devices such as smartphones, security cameras and cars. This thesis investigates different neural network compression techniques to see which wi
