21 citations · 122 across the 18 of their papers we have counts for
4 papers · 1 filter
Okapi: Generalising Better by Making Statistical Matches Match
Myles Bartlett, Sara Romiti, Viktoriia Sharmanska +1
We propose Okapi, a simple, efficient, and general method for robust semi-supervised learning based on online statistical matching. Our method uses a nearest-neighbours-based match…
RealPatch: A Statistical Matching Framework for Model Patching with Real Samples
Sara Romiti, Christopher Inskip, Viktoriia Sharmanska +1
Machine learning classifiers are typically trained to minimise the average error across a dataset. Unfortunately, in practice, this process often exploits spurious correlations cau…
Contrastive Examples for Addressing the Tyranny of the Majority
Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell +1
Computer vision algorithms, e.g. for face recognition, favour groups of individuals that are better represented in the training data. This happens because of the generalization tha…
Learning to Transfer Privileged Information
Viktoriia Sharmanska, Novi Quadrianto, Christoph H. Lampert
We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching…