no code implementations • 4 Aug 2023 • Martin Bikandi, Gorka Velez, Naiara Aginako, Itziar Irigoien
Anomaly detection, or outlier detection, is a crucial task in various domains to identify instances that significantly deviate from established patterns or the majority of data.
no code implementations • 12 Jun 2023 • Unai Muñoz-Aseguinolaza, Basilio Sierra, Naiara Aginako
The popularity of data augmentation techniques in machine learning has increased in recent years, as they enable the creation of new samples from existing datasets.
no code implementations • 29 Jun 2021 • Fadi Boutros, Naser Damer, Jan Niklas Kolf, Kiran Raja, Florian Kirchbuchner, Raghavendra Ramachandra, Arjan Kuijper, Pengcheng Fang, Chao Zhang, Fei Wang, David Montero, Naiara Aginako, Basilio Sierra, Marcos Nieto, Mustafa Ekrem Erakin, Ugur Demir, Hazim Kemal, Ekenel, Asaki Kataoka, Kohei Ichikawa, Shizuma Kubo, Jie Zhang, Mingjie He, Dan Han, Shiguang Shan, Klemen Grm, Vitomir Štruc, Sachith Seneviratne, Nuran Kasthuriarachchi, Sanka Rasnayaka, Pedro C. Neto, Ana F. Sequeira, Joao Ribeiro Pinto, Mohsen Saffari, Jaime S. Cardoso
These teams successfully submitted 18 valid solutions.
no code implementations • 20 Apr 2021 • David Montero, Marcos Nieto, Peter Leskovsky, Naiara Aginako
Experimental results show that the proposed approach highly boosts the original model accuracy when dealing with masked faces, while preserving almost the same accuracy on the original non-masked datasets.
no code implementations • 31 Mar 2021 • David Montero, Naiara Aginako, Basilio Sierra, Marcos Nieto
In this work, we address the problem of large-scale online face clustering: given a continuous stream of unknown faces, create a database grouping the incoming faces by their identity.