3 papers
cs.LG2026
MahaVar: OOD Detection via Class-wise Mahalanobis Distance Variance under Neural Collapse
Donghwan Kim, Hyunsoo Yoon
Out-of-distribution (OOD) detection is a critical component for ensuring the reliability of deep neural networks in safety-critical applications. In this work, we present a key emp…
cs.LG2026
Why the Counterintuitive Phenomenon of Likelihood Rarely Appears in Tabular Anomaly Detection with Deep Generative Models?
Donghwan Kim, Junghun Phee, Hyunsoo Yoon
Deep generative models with tractable and analytically computable likelihoods, exemplified by normalizing flows, offer an effective basis for anomaly detection through likelihood-b…
cs.LG2026
Mitigating the Likelihood Paradox in Flow-based OOD Detection via Entropy Manipulation
Donghwan Kim, Hyunsoo Yoon
Deep generative models that can tractably compute input likelihoods, including normalizing flows, often assign unexpectedly high likelihoods to out-of-distribution (OOD) inputs. We…