1 citations · 1 across the 1 of their papers we have counts for
4 papers
Class-Balanced Active Learning for Image Classification
Javad Zolfaghari Bengar, Joost van de Weijer, Laura Lopez Fuentes +1
Active learning aims to reduce the labeling effort that is required to train algorithms by learning an acquisition function selecting the most relevant data for which a label shoul…
Reducing Label Effort: Self-Supervised meets Active Learning
Javad Zolfaghari Bengar, Joost van de Weijer, Bartlomiej Twardowski +1
Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to redu…
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning
Javad Zolfaghari Bengar, Bogdan Raducanu, Joost van de Weijer
Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring th…
Temporal Coherence for Active Learning in Videos
Javad Zolfaghari Bengar, Abel Gonzalez-Garcia, Gabriel Villalonga +5
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Th…