Towards Deep Clustering of Human Activities from Wearables

no code implementations • 2 Aug 2020 • Alireza Abedin, Farbod Motlagh, Qinfeng Shi, Seyed Hamid Rezatofighi, Damith Chinthana Ranasinghe

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 learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data.

Clustering Deep Clustering +2

Unsupervised Task Design to Meta-Train Medical Image Classifiers

no code implementations • 17 Jul 2019 • Gabriel Maicas, Cuong Nguyen, Farbod Motlagh, Jacinto C. Nascimento, Gustavo Carneiro

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 small training sets).

Classification Few-Shot Learning +1