27 citations · 40 across the 4 of their papers we have counts for
4 papers
Model-Agnostic Learning to Meta-Learn
Arnout Devos, Yatin Dandi
In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting t…
Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
Carlos Medina, Arnout Devos, Matthias Grossglauser
Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training. Recently, unsupervised meta-learning methods have exchange…
Revisiting Few-Shot Learning for Facial Expression Recognition
Anca-Nicoleta Ciubotaru, Arnout Devos, Behzad Bozorgtabar +2
Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of training data has the biggest imp…
Regression Networks for Meta-Learning Few-Shot Classification
Arnout Devos, Matthias Grossglauser
We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number…