Podrobnosti studentského projektu

Téma:Deep metric learning without hard positive examples
Katedra:Katedra kybernetiky
Vedoucí:Georgios Tolias, Ph.D.
Vypsáno jako:Diplomová práce, Bakalářská práce, Semestrální projekt
Popis:The goal of this project is to improve fully supervised deep metric learning that relies on category-level annotations. The large intra-category variability makes most of within category pairs not appropriate to use as positive pairs in the training process. The structure of the feature space, as given by the current state of the deep network, will be used to identify such difficult examples and exclude them from the training.
Literatura:Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese, Deep Metric Learning via Lifted Structured Feature Embedding, CVPR 2016
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondřej Chum, Mining on Manifolds: Metric Learning without Labels, CVPR, 2018.
Za obsah zodpovídá: Petr Pošík