Meta Mirror Descent: Optimiser Learning for Fast Convergence

no code implementations • 5 Mar 2022 • Boyan Gao, Henry Gouk, Hae Beom Lee, Timothy M. Hospedales

The resulting framework, termed Meta Mirror Descent (MetaMD), learns to accelerate optimisation speed.

Meta-Learning

Loss Function Learning for Domain Generalization by Implicit Gradient

no code implementations • 29 Sep 2021 • Boyan Gao, Henry Gouk, Yongxin Yang, Timothy Hospedales

We take a different approach, and explore the impact of the ERM loss function on out-of-domain generalisation.

Domain Generalization Meta-Learning

Searching for Robustness: Loss Learning for Noisy Classification Tasks

no code implementations • ICCV 2021 • Boyan Gao, Henry Gouk, Timothy M. Hospedales

We present a "learning to learn" approach for automatically constructing white-box classification loss functions that are robust to label noise in the training data.

Classification General Classification

Deep clustering with concrete k-means

no code implementations • 17 Oct 2019 • Boyan Gao, Yongxin Yang, Henry Gouk, Timothy M. Hospedales

We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential of deep k-means to outperform traditional two-step feature extraction and shallow-clustering strategies.

Clustering Deep Clustering