Online domain adaptation for continuous cross-subject liver viability evaluation based on irregular thermal data

Hajifar, Sahand; Sun, Hongyue

doi:10.1080/24725854.2021.1949762

Cited by 5 publications

(8 citation statements)

References 28 publications

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“…Although both UDA and online learning have been extensively explored, few studies focus on OUDA. Existing OUDA studies [Mancini et al, 2018;Moon et al, 2020;Hajifar and Sun, 2021] are majorly application-driven. [Moon et al, 2020] proposes an intuitive multi-step framework that computes the mean-target subspace by the geometrical interpretation of the euclidean space.…”

Section: Realated Workmentioning

confidence: 99%

“…[Moon et al, 2020] proposes an intuitive multi-step framework that computes the mean-target subspace by the geometrical interpretation of the euclidean space. [Mancini et al, 2018] focuses on OUDA robotic kitting in unconstrained scenarios while [Hajifar and Sun, 2021] studies the continuous cross-subject liver viability evaluation. The theoretical analysis for OUDA is still vacant.…”

Section: Realated Workmentioning

confidence: 99%

“…Although UDA has been extensively explored, few studies focus on OUDA. Existing OUDA studies [Moon et al, 2020;Hajifar and Sun, 2021] are majorly driven by applications while lacking theoretical guarantees. Existing UDA theories [Ben-David et al, 2007; are also insufficient to cover OUDA which suffers from catastrophic forgetting.…”

Section: Introductionmentioning

confidence: 99%

“…To minimize the proposed generator discrepancy, a Source Feature Distillation (SFD) method has been proposed which utilizes the source-only model as a teacher to guide OUDA algorithms. In the experiments, we choose two classical UDA methods, i.e., DANN [Ganin et al, 2016] and CDAN [Long et al, 2017] and one state-of-the-art (SOTA) UDA method [Jin et al, 2020] as the baselines. The experiments are conducted with real-world datasets, i.e., Office-31, Office-home and ImageCLEF-DA.…”

Section: Introductionmentioning

confidence: 99%

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Learning Unforgotten Domain-Invariant Representations for Online Unsupervised Domain Adaptation

Galib

McDonald

Wilson

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep neural network (DNN) with generalized extreme value (GEV) distribution to forecast the block maximum value of a time series. Implementing such a network is a challenge as the framework must preserve the inter-dependent constraints among the GEV model parameters even when the DNN is initialized. We describe our approach to address this challenge and present an architecture that enables both conditional mean and quantile prediction of the block maxima. The extensive experiments performed on both real-world and synthetic data demonstrated the superiority of DeepExtrema compared to other baseline methods.

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Section: Realated Workmentioning

confidence: 99%

Section: Realated Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning Unforgotten Domain-Invariant Representations for Online Unsupervised Domain Adaptation

Galib

McDonald

Wilson

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…However, we do not claim to have proposed the task of online unsupervised domain adaptation, which has existed before the emergence of deep learning [13,29,51]. The more recent works are either engineered for a specific task that lacks generality [10,15,23,48,93] or more general to compare to but not published [80]. Yet, we try to compare to the unpublished approach [80] despite its limited availability.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Burn After Reading: Online Adaptation for Cross-domain Streaming Data

Yang¹,

Gao²,

Chen³

et al. 2021

Preprint

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In the context of online privacy, many methods propose complex privacy and security preserving measures to protect sensitive data. In this paper we argue that: not storing any sensitive data is the best form of security. Thus we propose an online framework that "burns after reading", i.e. each online sample is immediately deleted after it is processed. Meanwhile, we tackle the inevitable distribution shift between the labeled public data and unlabeled private data as a problem of unsupervised domain adaptation. Specifically, we propose a novel algorithm that aims at the most fundamental challenge of the online adaptation setting-the lack of diverse source-target data pairs. Therefore, we design a Cross-Domain Bootstrapping approach, called CRODOBO, to increase the combined diversity across domains. Further, to fully exploit the valuable discrepancies among the diverse combinations, we employ the training strategy of multiple learners with cosupervision. CRODOBO achieves state-of-the-art online performance on four domain adaptation benchmarks.

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Human reliability modeling in occupational environments toward a safe and productive operator 4.0

Kheiri

Vahedi

Sun

et al. 2023

International Journal of Industrial Ergonomics

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Online domain adaptation for continuous cross-subject liver viability evaluation based on irregular thermal data

Cited by 5 publications

References 28 publications

Learning Unforgotten Domain-Invariant Representations for Online Unsupervised Domain Adaptation

Learning Unforgotten Domain-Invariant Representations for Online Unsupervised Domain Adaptation

Burn After Reading: Online Adaptation for Cross-domain Streaming Data

Human reliability modeling in occupational environments toward a safe and productive operator 4.0

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