104 citations · 105 across the 6 of their papers we have counts for
6 papers
On Pitfalls of Measuring Occlusion Robustness through Data Distortion
Antonia Marcu
Over the past years, the crucial role of data has largely been shadowed by the field's focus on architectures and training procedures. We often cause changes to the data without be…
Generalisation and the Risk--Entropy Curve
Dominic Belcher, Antonia Marcu, Adam Prügel-Bennett
In this paper we show that the expected generalisation performance of a learning machine is determined by the distribution of risks or equivalently its logarithm -- a quantity we t…
On Data-centric Myths
Antonia Marcu, Adam Prügel-Bennett
The community lacks theory-informed guidelines for building good data sets. We analyse theoretical directions relating to what aspects of the data matter and conclude that the intu…
On the Effects of Artificial Data Modification
Antonia Marcu, Adam Prügel-Bennett
Data distortion is commonly applied in vision models during both training (e.g methods like MixUp and CutMix) and evaluation (e.g. shape-texture bias and robustness). This data mod…
FMix: Enhancing Mixed Sample Data Augmentation
Ethan Harris, Antonia Marcu, Matthew Painter +3
Mixed Sample Data Augmentation (MSDA) has received increasing attention in recent years, with many successful variants such as MixUp and CutMix. By studying the mutual information…
Rethinking Generalisation
Antonia Marcu, Adam Prügel-Bennett
In this paper, a new approach to computing the generalisation performance is presented that assumes the distribution of risks, , for a learning scenario is known. From this,…