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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 7: AAAI-22 Technical Tracks 7

iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection

February 1, 2023

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Authors

Ramneet Kaur

Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA


Susmit Jha

Computer Science Laboratory, SRI International, Menlo Park, USA


Anirban Roy

Computer Science Laboratory, SRI International, Menlo Park, USA


Sangdon Park

Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA School of Computer Science, Georgia Institute of Technology


Edgar Dobriban

Statistics & Computer Science, University of Pennsylvania


Oleg Sokolsky

Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA


Insup Lee

Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA


DOI:

10.1609/aaai.v36i7.20670


Abstract:

Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence on inputs outside their training distribution. The deployment of DNNs in safety-critical domains requires detection of out-of-distribution (OOD) data so that DNNs can abstain from making predictions on those. A number of methods have been recently developed for OOD detection, but there is still room for improvement. We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection. It relies on a novel base non-conformity measure and a new aggregation method, used in the inductive conformal anomaly detection framework, thereby guaranteeing a bounded false detection rate. We demonstrate the efficacy of iDECODe by experiments on image and audio datasets, obtaining state-of-the-art results. We also show that iDECODe can detect adversarial examples. Code, pre-trained models, and data are available at https://github.com/ramneetk/iDECODe.

Topics: AAAI

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HOW TO CITE:

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection Proceedings of the AAAI Conference on Artificial Intelligence (2022) 7104-7114.

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection AAAI 2022, 7104-7114.

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee (2022). iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 7104-7114.

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee. iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.7104-7114.

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee. 2022. iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection. "Proceedings of the AAAI Conference on Artificial Intelligence". 7104-7114.

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee. (2022) "iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection", Proceedings of the AAAI Conference on Artificial Intelligence, p.7104-7114

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee, "iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection", AAAI, p.7104-7114, 2022.

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee. "iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.7104-7114.

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee. "iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 7104-7114.

Ramneet Kaur||Susmit Jha||Anirban Roy||Sangdon Park||Edgar Dobriban||Oleg Sokolsky||Insup Lee. iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection. AAAI[Internet]. 2022[cited 2023]; 7104-7114.


ISSN: 2374-3468


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