What is recorded here?
Fits class means and a tied feature covariance, then combines layer-wise Mahalanobis scores with input preprocessing to detect anomalous or adversarial examples from pretrained representations.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Gaussian-discriminant interpretation is an assumption/model, not proof that arbitrary learned features are Gaussian. Layer ensemble weights and preprocessing use validation data; empirical detection is not certified resistance to adaptive attacks. Mahalanobis distance itself is inherited.
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People and systems
Kimin Lee; Kibok Lee; Honglak Lee; Jinwoo Shin
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Mahalanobis feature-distance detection for anomalies and adversarial samples
Fits class means and a tied feature covariance, then combines layer-wise Mahalanobis scores with input preprocessing to detect anomalous or adversarial examples from pretrained representations.
Scientific scope
Gaussian-discriminant interpretation is an assumption/model, not proof that arbitrary learned features are Gaussian. Layer ensemble weights and preprocessing use validation data; empirical detection is not certified resistance to adaptive attacks. Mahalanobis distance itself is inherited.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Kimin Lee; Kibok Lee; Honglak Lee; Jinwoo Shin
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.