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

Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, we would like to investigate various state-of-the-art data selection mechanisms from a hardware requirements and …
Supervisors: Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data selection, deep learning, machine learning, resource efficiency