A robust variational autoencoder using beta divergence.

Akrami, Haleh; Joshi, Anand A; Li, Jian; Aydöre, Sergül; Leahy, Richard M

Akrami, Haleh; Joshi, Anand A; Li, Jian; Aydöre, Sergül; Leahy, Richard M.

Afiliação

Akrami H; Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA.
Joshi AA; Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA.
Li J; Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Aydöre S; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Leahy RM; Amazon Web Services, New York, NY, USA.

Knowl Based Syst ; 2382022 Feb 28.

Article em En | MEDLINE | ID: mdl-36714396

Palavras-chave

Outlier; RVAE; Robust anomaly detection; VAE; ß divergence

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Knowl Based Syst Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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