May 15, 2024, 4:42 a.m. | Claus Hofmann, Simon Schmid, Bernhard Lehner, Daniel Klotz, Sepp Hochreiter

cs.LG updates on

arXiv:2405.08766v1 Announce Type: new
Abstract: Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the training process, can drastically improve OOD detection performance compared to approaches without advanced training strategies. We introduce Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy (MHE) to sharpen the decision boundary between the in-distribution and OOD data. Hopfield Boosting encourages the model to concentrate on hard-to-distinguish auxiliary outlier examples …

abstract advanced arxiv boosting cs.lg data detection distribution energy machine machine learning machine learning models modern outlier performance process strategies training type world

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