23 citations · 109 across the 19 of their papers we have counts for
20 papers
A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning
Minyoung Kim, Timothy Hospedales
We propose a novel hierarchical Bayesian model for learning with a large (possibly infinite) number of tasks/episodes, which suits well the few-shot meta learning problem. We consi…
FedHB: Hierarchical Bayesian Federated Learning
Minyoung Kim, Timothy Hospedales
We propose a novel hierarchical Bayesian approach to Federated Learning (FL), where our model reasonably describes the generative process of clients' local data via hierarchical Ba…
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
Minyoung Kim, Da Li, Timothy Hospedales
We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case target domain and adapt a model to t…
Fisher SAM: Information Geometry and Sharpness Aware Minimisation
Minyoung Kim, Da Li, Shell Xu Hu +1
Recent sharpness-aware minimisation (SAM) is known to find flat minima which is beneficial for better generalisation with improved robustness. SAM essentially modifies the loss fun…
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
Shell Xu Hu, Da Li, Jan Stühmer +2
Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning…
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation
Minyoung Kim, Timothy Hospedales
The meta learning few-shot classification is an emerging problem in machine learning that received enormous attention recently, where the goal is to learn a model that can quickly…