Orthogonal Approximation of Marginal Likelihood of Generative Models

0522204 - ÚTIA 2021 RIV CA eng C - Konferenční příspěvek (zahraniční konf.)
Šmídl, Václav - Bím, J. - Pevný, T.
Orthogonal Approximation of Marginal Likelihood of Generative Models.
Bayesian Deep Learning NeurIPS 2019 Workshop. Vancouver: University of Oxford Computer Science department, 2019, č. článku 48..
[NeurIPS 2019. Vancouver (CA), 08.12.2019-14.12.2019]
Grant CEP: GA ČR GA18-21409S
Institucionální podpora: RVO:67985556
Klíčová slova: approximation * generative models * orthogonal combinations
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2020/AS/smidl-0522204.pdf

This paper presents a new approximation of the marginal likelihood of generative models which is used as a score for anomaly detection. The score is motivated by the shortcoming of the popular reconstruction error that it can behave arbitrarily outside the known samples. The proposed score corrects this by orthogonal combination of the reconstruction error and the likelihood in the latent space. As experimentally shown on benchmark problems from anomaly detection and illustrated on a toy problem, this combination lends the score robustness to outliers. Generative models evaluated with this score outperformed the competing methods especially in tasks of learning distribution from data corrupted by anomalies. Finally, the score is compatible with contemporary generative models, namely variational auto-encoders and generative adversarial networks
Trvalý link: http://hdl.handle.net/11104/0308914