3 papers
eess.SP2020
Towards Deep Clustering of Human Activities from Wearables
Alireza Abedin, Farbod Motlagh, Qinfeng Shi +2
Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learni…
cs.CV2019
Unsupervised Task Design to Meta-Train Medical Image Classifiers
Gabriel Maicas, Cuong Nguyen, Farbod Motlagh +2
Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with smal…
cs.CV2018
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh +4
Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which…