3 citations · 4 across the 4 of their papers we have counts for
5 papers
On the Bayes Inconsistency of Disagreement Discrepancy Surrogates
Neil G. Marchant, Andrew C. Cullen, Feng Liu +1
Deep neural networks often fail when deployed in real-world contexts due to distribution shift, a critical barrier to building safe and reliable systems. An emerging approach to ad…
SupLID: Geometrical Guidance for Out-of-Distribution Detection in Semantic Segmentation
Nimeshika Udayangani, Sarah Erfani, Christopher Leckie
Out-of-Distribution (OOD) detection in semantic segmentation aims to localize anomalous regions at the pixel level, advancing beyond traditional image-level OOD techniques to bette…
Exploiting Inter-Sample Information for Long-tailed Out-of-Distribution Detection
Nimeshika Udayangani, Hadi M. Dolatabadi, Sarah Erfani +1
Detecting out-of-distribution (OOD) data is essential for safe deployment of deep neural networks (DNNs). This problem becomes particularly challenging in the presence of long-tail…
Intention-aware Hierarchical Diffusion Model for Long-term Trajectory Anomaly Detection
Chen Wang, Sarah Erfani, Tansu Alpcan +1
Long-term trajectory anomaly detection is a challenging problem due to the diversity and complex spatiotemporal dependencies in trajectory data. Existing trajectory anomaly detecti…
Position: Certified Robustness Does Not (Yet) Imply Model Security
Andrew C. Cullen, Paul Montague, Sarah M. Erfani +1
While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be…