10 citations · 21 across the 5 of their papers we have counts for
13 papers · 1 filter
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…
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…
Saliency for free: Saliency prediction as a side-effect of object recognition
Carola Figueroa-Flores, David Berga, Joost van der Weijer +1
Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects. Neural networks for saliency estimation require ground truth salien…
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets
Yaxing Wang, Hector Laria Mantecon, Joost van de Weijer +2
Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important chall…