no code implementations • ICLR 2022 • Lucas Deecke, Timothy Hospedales, Hakan Bilen
A fundamental shortcoming of deep neural networks is their specialization to a single task and domain.
no code implementations • 1 Jan 2021 • Lucas Deecke, Timothy Hospedales, Hakan Bilen
A fundamental shortcoming of deep neural networks is their specialization to a single task and domain.
no code implementations • 5 Oct 2020 • Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality.
no code implementations • 1 Jun 2020 • Lucas Deecke, Timothy Hospedales, Hakan Bilen
While recent techniques in domain adaptation and multi-domain learning enable the learning of more domain-agnostic features, their success relies on the presence of domain labels, typically requiring manual annotation and careful curation of datasets.
2 code implementations • ICLR 2019 • Lucas Deecke, Iain Murray, Hakan Bilen
Normalization methods are a central building block in the deep learning toolbox.
1 code implementation • ICML 2018 • Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft
Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.
Ranked #32 on Anomaly Detection on One-class CIFAR-10
no code implementations • ICLR 2018 • Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft
Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.