6 papers
Transfer learning RGB models to hyperspectral images with trainable tensor decompositions
Mariette Schönfeld, Laurens Devos, Wannes Meert +1
Transfer learning makes it possible to use large vision networks on a variety of domains, by specializing their models' general filters to new tasks. However, these networks assume…
Tailored Transformation Invariance for Industrial Anomaly Detection
Mariette Schönfeld, Wannes Meert, Hendrik Blockeel
Industrial Anomaly Detection (IAD) is a subproblem within Computer Vision Anomaly Detection that has been receiving increasing amounts of attention due to its applicability to real…
Warping and Matching Subsequences Between Time Series
Simiao Lin, Wannes Meert, Pieter Robberechts +1
Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparis…
Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery
Aras Yurtman, Daan Van Wesenbeeck, Wannes Meert +1
Time Series Motif Discovery (TSMD) identifies repeating patterns in time series data, but its unsupervised nature might result in motifs that are not interesting to the user. To ad…
Quantitative Evaluation of Motif Sets in Time Series
Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert +1
Time Series Motif Discovery (TSMD), which aims at finding recurring patterns in time series, is an important task in numerous application domains, and many methods for this task ex…
Fast and interpretable electricity consumption scenario generation for individual consumers
J. Soenen, A. Yurtman, T. Becker +2
To enable the transition from fossil fuels towards renewable energy, the low-voltage grid needs to be reinforced at a faster pace and on a larger scale than was historically the ca…