Deep convolutional networks are able to learn representation of images, scoring well in tasks such as image classification and object detection. During model training, these networks have the ability to process different input sizes without requiring changes to their architecture. In this project, we would like to investigate the effects that changing input sizes has on these kinds of models. We …
Supervisors: Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data attribution, deep learning, machine learning, resource efficiency