584 citations · 708 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…