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 [BibTeX] [Marc21]
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree
Type of publication: Conference paper
Citation: Weinshall_NIPS_2008
Booktitle: Advances in Neural Information Processing Systems 21
Year: 2008
Month: 12
Abstract: Unexpected stimuli are a challenge to any machine learning algorithm. Here we identify distinct types of unexpected events, focusing on ’incongruent events’ - when ’general level’ and ’specific level’ classifiers give conflicting predictions. We define a formal framework for the representation and processing of incongruent events: starting from the notion of label hierarchy, we show how partial order on labels can be deduced from such hierarchies. For each event, we compute its probability in different ways, based on adjacent levels (according to the partial order) in the label hierarchy. An incongruent event is an event where the probability computed based on some more specific level (in accordance with the partial order) is much smaller than the probability computed based on some more general level, leading to conflicting predictions. We derive algorithms to detect incongruent events from different types of hierarchies, corresponding to class membership or part membership. Respectively, we show promising results with real data on two specific problems: Out Of Vocabulary words in speech recognition, and the identification of a new sub-class (e.g., the face of a new individual) in audio-visual facial object recognition.
Projects DIRAC
Authors Weinshall, Daphna
Hermansky, Hynek
Zweig, Alon
Luo, Jie
Jimison, Holly
Ohl, Frank
Pavel, Misha
Added by: [UNK]
Total mark: 0
  • Weinshall_NIPS_2008.pdf