4 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
Is Task-Specific Training Necessary for Anomaly Detection?
Xingwu Zhang, Guanxuan Li, Paul Henderson +2
Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on training encoder--decoder models to reconstruct anomaly-free features. However, we argue…
3D-ADAM: A Dataset for 3D Anomaly Detection in Additive Manufacturing
Paul McHard, Florent P. Audonnet, Oliver Summerell +3
Surface defects are a primary source of yield loss in manufacturing, yet existing anomaly detection methods often fail in real-world deployment due to limited and unrepresentative…
Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning
Zijun Long, Lipeng Zhuang, George Killick +3
Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-rese…
Foveation in the Era of Deep Learning
George Killick, Paul Henderson, Paul Siebert +1
In this paper, we tackle the challenge of actively attending to visual scenes using a foveated sensor. We introduce an end-to-end differentiable foveated active vision architecture…
Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning
Zijun Long, George Killick, Lipeng Zhuang +3
Image classification datasets exhibit a non-negligible fraction of mislabeled examples, often due to human error when one class superficially resembles another. This issue poses ch…