11 citations · 15 across the 2 of their papers we have counts for
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cs.CV2023
Real time dense anomaly detection by learning on synthetic negative data
Anja Delić, Matej Grcić, Siniša Šegvić
Most approaches to dense anomaly detection rely on generative modeling or on discriminative methods that train with negative data. We consider a recent hybrid method that optimizes…
cs.CV2022★ 4 cited
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
Matej Grcić, Petra Bevandić, Siniša Šegvić
Anomaly detection can be conceived either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches ex…
cs.CV2020
Dense open-set recognition with synthetic outliers generated by Real NVP
Matej Grcić, Petra Bevandić, Siniša Šegvić
Today's deep models are often unable to detect inputs which do not belong to the training distribution. This gives rise to confident incorrect predictions which could lead to devas…