Meta-learning in neural networks: A survey T Hospedales, A Antoniou, P Micaelli, A Storkey IEEE transactions on pattern analysis and machine intelligence 44 (9), 5149-5169, 2021 | 2378 | 2021 |
Data Augmentation Generative Adversarial Networks A Antoniou, A Storkey, H Edwards ICANN 2018, 2017 | 1527* | 2017 |
How to train your MAML A Antoniou, H Edwards, A Storkey ICLR 2019, 2018 | 952 | 2018 |
CINIC-10 is not ImageNet or CIFAR-10 LN Darlow, EJ Crowley, A Antoniou, AJ Storkey arXiv preprint arXiv:1810.03505, 2018 | 466 | 2018 |
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation A Antoniou, A Storkey arXiv preprint arXiv:1902.09884, 2019 | 91 | 2019 |
Learning to Learn by Self-Critique A Antoniou, AJ Storkey NeurIPS 2019, 9936-9946, 2019 | 88 | 2019 |
Defining benchmarks for continual few-shot learning A Antoniou, M Patacchiola, M Ochal, A Storkey Meta-Learrning Workshop, NeurIPS 2020, 2020 | 53 | 2020 |
A general purpose intelligent surveillance system for mobile devices using deep learning A Antoniou, P Angelov 2016 International Joint Conference on Neural Networks (IJCNN), 2879-2886, 2016 | 23 | 2016 |
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging A Fontanella, A Antoniou, W Li, J Wardlaw, G Mair, E Trucco, A Storkey ICML 2023, 2023 | 11 | 2023 |
Dilated Densenets for Relational Reasoning A Antoniou, A Słowik, EJ Crowley, A Storkey arXiv preprint arXiv:1811.00410, 2018 | 6 | 2018 |
Contrastive Meta-Learning for Partially Observable Few-Shot Learning A Jelley, A Storkey, A Antoniou, S Devlin ICLR 2023, 2023 | 5 | 2023 |
Challenges of building medical image datasets for development of deep learning software in stroke A Fontanella, W Li, G Mair, A Antoniou, E Platt, C Martin, P Armitage, ... arXiv preprint arXiv:2309.15081, 2023 | 1 | 2023 |
Meta learning for supervised and unsupervised few-shot learning A Antoniou The University of Edinburgh, 2021 | 1 | 2021 |
Meta-meta-learning for Neural Architecture Search through arXiv Descent A Antoniou, N Pawlowski, J Turner, J Owers, J Mellor, EJ Crowley SIGBOVIK, 2019 | 1 | 2019 |
Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT A Fontanella, W Li, G Mair, A Antoniou, E Platt, P Armitage, E Trucco, ... Stroke and Vascular Neurology, 2024 | | 2024 |
einspace: Searching for Neural Architectures from Fundamental Operations L Ericsson, M Espinosa, C Yang, A Antoniou, A Storkey, SB Cohen, ... arXiv preprint arXiv:2405.20838, 2024 | | 2024 |
EEVEE and GATE: Finding the right benchmarks and how to run them seamlessly A Antoniou, E Triantafillou, H Larochelle, S Montella, F Rezk, K Kim, ... | | 2024 |
Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution Shifts K Kim, SN Gowda, P Eustratiadis, A Antoniou, RB Fisher arXiv preprint arXiv:2401.11406, 2024 | | 2024 |
Liouna: Biologically Plausible Learning for Efficient Pre-Training of Transferrable Deep Models F Rezk, A Antoniou, H Gouk, T Hospedales 2nd Workshop on Advancing Neural Network Training: Computational Efficiency …, 2024 | | 2024 |
Is Scaling Learned Optimizers Worth It? Evaluating The Value of VeLO’s 4000 TPU Months F Rezk, A Antoniou, H Gouk, T Hospedales Proceedings on, 65-83, 2023 | | 2023 |