Best paper award at #CVPR2018 :
"Taskonomy: Disentangling Task Transfer Learning"
Abstract : Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks (…).
Paper: https://arxiv.org/pdf/1804.08328.pdf
Data: http://taskonomy.stanford.edu
