Orthogonal Approximation of Marginal Likelihood of Generative Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00339857" target="_blank" >RIV/68407700:21230/19:00339857 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/19:00522204
Result on the web
<a href="http://bayesiandeeplearning.org/2019/papers/48.pdf" target="_blank" >http://bayesiandeeplearning.org/2019/papers/48.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Orthogonal Approximation of Marginal Likelihood of Generative Models
Original language description
This paper presents a new approximation of the marginal likelihood of generativemodels which is used as a score for anomaly detection. The score is motivatedby the shortcoming of the popular reconstruction error that it can behave arbitrar-ily outside the known samples. The proposed score corrects this by orthogonalcombination of the reconstruction error and the likelihood in the latent space. Asexperimentally shown on benchmark problems from anomaly detection and illus-trated on a toy problem, this combination lends the score robustness to outliers.Generative models evaluated with this score outperformed the competing meth-ods especially in tasks of learning distribution from data corrupted by anomalies.Finally, the score is compatible with contemporary generative models, namelyvariational auto-encoders and generative adversarial networks.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů