no code implementations • 14 Sep 2023 • Francesco Fabbri, Xianghang Liu, Jack R. McKenzie, Bartlomiej Twardowski, Tri Kurniawan Wijaya
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy.
1 code implementation • 12 Sep 2023 • Alex Gomez-Villa, Bartlomiej Twardowski, Kai Wang, Joost Van de Weijer
In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network.
1 code implementation • 12 Feb 2023 • Alejandro Ariza-Casabona, Bartlomiej Twardowski, Tri Kurniawan Wijaya
This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation.
1 code implementation • 30 Dec 2021 • Alex Gomez-Villa, Bartlomiej Twardowski, Lu Yu, Andrew D. Bagdanov, Joost Van de Weijer
Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches.
no code implementations • 25 Aug 2021 • Javad Zolfaghari Bengar, Joost Van de Weijer, Bartlomiej Twardowski, Bogdan Raducanu
Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high.
1 code implementation • 28 Oct 2020 • Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, Joost Van de Weijer
For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning.
1 code implementation • CVPR 2020 • Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa, Bartlomiej Twardowski, Joost Van de Weijer
Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification.