11 citations · 15 across the 2 of their papers we have counts for
3 papers
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.LG2021★ 11 cited
Densely connected normalizing flows
Matej Grcić, Ivan Grubišić, Siniša Šegvić
Normalizing flows are bijective mappings between inputs and latent representations with a fully factorized distribution. They are very attractive due to exact likelihood valuation…
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…