35 citations · 62 across the 2 of their papers we have counts for
6 papers
Defining Benchmarks for Continual Few-Shot Learning
Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal +1
Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks. That being said, the field has still to frame a…
Meta-Learning in Neural Networks: A Survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli +1
The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratc…
Learning to learn via Self-Critique
Antreas Antoniou, Amos Storkey
In few-shot learning, a machine learning system learns from a small set of labelled examples relating to a specific task, such that it can generalize to new examples of the same ta…
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…
How to train your MAML
Antreas Antoniou, Harrison Edwards, Amos Storkey
The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic…