1 code implementation • 19 Oct 2022 • Georgios Rizos, Jenna Lawson, Simon Mitchell, Pranay Shah, Xin Wen, Cristina Banks-Leite, Robert Ewers, Bjoern W. Schuller
We focus on using the predictive uncertainty signal calculated by Bayesian neural networks to guide learning in the self-same task the model is being trained on.
no code implementations • 22 Jun 2022 • Meishu Song, Zijiang Yang, Andreas Triantafyllopoulos, Xin Jing, Vincent Karas, Xie Jiangjian, Zixing Zhang, Yamamoto Yoshiharu, Bjoern W. Schuller
We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge.
no code implementations • 2 Mar 2022 • Shuo Liu, Adria Mallol-Ragolta, Emilia Parada-Cabeleiro, Kun Qian, Xin Jing, Alexander Kathan, Bin Hu, Bjoern W. Schuller
Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task.
no code implementations • 31 Dec 2021 • Toby Godwin, Georgios Rizos, Alice Baird, Najla D. Al Futaisi, Vincent Brisse, Bjoern W. Schuller
We achieve this by measuring the change in predictive performance of a music mood/theme classifier after augmenting its training data with generated samples.
no code implementations • 10 Sep 2015 • George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjoern W. Schuller
Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation.