3 citations · 4 across the 4 of their papers we have counts for
4 papers
Modularity Trumps Invariance for Compositional Robustness
Ian Mason, Anirban Sarkar, Tomotake Sasaki +1
By default neural networks are not robust to changes in data distribution. This has been demonstrated with simple image corruptions, such as blurring or adding noise, degrading ima…
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Anirban Sarkar, Matthew Groth, Ian Mason +2
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios. In this paper, we introduce a tool to visualize and understand such failures. We draw inspiration from conc…
Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases
Ian Mason, Sebastian Starke, Taku Komura
Controlling the manner in which a character moves in a real-time animation system is a challenging task with useful applications. Existing style transfer systems require access to…
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration
Cian Eastwood, Ian Mason, Christopher K. I. Williams +1
Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain dat…