5 citations · 6 across the 4 of their papers we have counts for
7 papers
Searching for Robustness: Loss Learning for Noisy Classification Tasks
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. We parameteriz…
How Well Do Self-Supervised Models Transfer?
Linus Ericsson, Henry Gouk, Timothy M. Hospedales
Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. We evaluate the transfer p…
Resolving Conflict in Decision-Making for Autonomous Driving
Jack Geary, Subramanian Ramamoorthy, Henry Gouk
Recent work on decision making and planning for autonomous driving has made use of game theoretic methods to model interaction between agents. We demonstrate that methods based on…
Altruistic Decision-Making for Autonomous Driving with Sparse Rewards
Jack Geary, Henry Gouk
In order to drive effectively, a driver must be aware of how they can expect other vehicles' behaviour to be affected by their decisions, and also how they are expected to behave b…
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights
Linus Ericsson, Henry Gouk, Timothy M. Hospedales
In the absence of large labelled datasets, self-supervised learning techniques can boost performance by learning useful representations from unlabelled data, which is often more re…
Distance-Based Regularisation of Deep Networks for Fine-Tuning
Henry Gouk, Timothy M. Hospedales, Massimiliano Pontil
We investigate approaches to regularisation during fine-tuning of deep neural networks. First we provide a neural network generalisation bound based on Rademacher complexity that u…