794 citations · 957 across the 13 of their papers we have counts for
3 papers · 1 filter
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation
Antreas Antoniou, Amos Storkey
The field of few-shot learning has been laboriously explored in the supervised setting, where per-class labels are available. On the other hand, the unsupervised few-shot learning…
Dilated DenseNets for Relational Reasoning
Antreas Antoniou, Agnieszka Słowik, Elliot J. Crowley +1
Despite their impressive performance in many tasks, deep neural networks often struggle at relational reasoning. This has recently been remedied with the introduction of a plug-in…
Data Augmentation Generative Adversarial Networks
Antreas Antoniou, Amos Storkey, Harrison Edwards
Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviate…