584 citations · 708 across the 14 of their papers we have counts for
17 papers
Continual Evidential Deep Learning for Out-of-Distribution Detection
Eduardo Aguilar, Bogdan Raducanu, Petia Radeva +1
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out a…
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
Francesco Pelosin, Saurav Jha, Andrea Torsello +2
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the k…
Transferring Unconditional to Conditional GANs with Hyper-Modulation
Héctor Laria, Yaxing Wang, Joost van de Weijer +1
GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-qu…
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…