Resource-efficient learning for vision-capable neural models - Tiong, Anthony Meng Huat (Nanyang Technological University, 2024)
The emblem of human intelligence is the ability to address a new task by applying relevant knowledge learned from previous tasks. Hence, human requires only a minimal number of examples from the new task in the adaptation process. In contrary, deep learning models still lag behind human in achieving such remarkable generalization capability to a new task which has limited data. The learning setting is termed as resource-efficient learning. In this thesis, we explore resource-efficient problem formulations for vision-capable deep learning models.