9 citations · 9 across the 1 of their papers we have counts for
5 papers
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…
On Class Orderings for Incremental Learning
Marc Masana, Bartłomiej Twardowski, Joost van de Weijer
The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incre…
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew D. Bagdanov +1
Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little atten…
Semantic Drift Compensation for Class-Incremental Learning
Lu Yu, Bartłomiej Twardowski, Xialei Liu +5
Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a tim…
Orderless Recurrent Models for Multi-label Classification
Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa +2
Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered f…