Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources

Wu, Qingyao; Wu, Hanrui; Zhou, Xiaoming; Tan, Mingkui; Xu, Yonghui; Yan, Yuguang; Hao, Tianyong

doi:10.1109/tkde.2017.2685597

Cited by 106 publications

(39 citation statements)

References 22 publications

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“…3) Multi Source Domain Knowledge Extraction for Astronomy: In this concept, [22] [26] the knowledge obtained from multiple source domains and transferred to a single target domain. [14] Assert that by building a model from multiple homogeneous source domains for knowledge transfer to the final model becomes more refined.…”

Section: Enhancing Space Research and Astronomy Learning Methodolomentioning

confidence: 99%

Online Transfer Learning and Organic Computing for Deep Space Research and Astronomy

Natarajan

2019

SSRN Journal

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Deep space exploration is the pillars within the field of outer space analysis and physical science. The amount of knowledge from numerous space vehicle and satellites orbiting the world of study are increasing day by day. This information collected from numerous experiences of the advanced space missions is huge. These information helps us to enhance current space knowledge and the experiences can be converted and transformed into segregated knowledge which helps us to explore and understand the realms of the deep space.. Online Transfer Learning (OTL) is a machine learning concept in which the knowledge gets transferred between the source domain and target domain in real time, in order to help train a classifier of the target domain. Online transfer learning can be an efficient method for transferring experiences and data gained from the space analysis data to a new learning task and can also routinely update the knowledge as the task evolves.

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Section: Enhancing Space Research and Astronomy Learning Methodolomentioning

confidence: 99%

Online Transfer Learning and Organic Computing for Deep Space Research and Astronomy

Natarajan

2019

SSRN Journal

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“…Domain Adaptation: There also exists another body of non-deep learning transfer paradigms that were often referred to as domain adaption. This however often include methods that not only assume access domain-specific [24,29,[36][37][38] and/or model-specific knowledge of the domains being adapted [17,20,25,27,28,35,41,43], but are also not applicable to deep learning models [10,39] with arbitrary architecture as addressed in our work.…”

Section: Related Workmentioning

confidence: 99%

CHEER: Rich Model Helps Poor Model via Knowledge Infusion

Xiao

Nghia

Hong

et al. 2022

IEEE Trans. Knowl. Data Eng.

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There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e.g., intensive care units). However, in many other practical situations, we can only access data with much fewer feature channels in a poor-data environments (e.g., at home), which often results in predictive models with poor performance. How can we boost the performance of models learned from such poor-data environment by leveraging knowledge extracted from existing models trained using rich data in a related environment? To address this question, we develop a knowledge infusion framework named CHEER that can succinctly summarize such rich model into transferable representations, which can be incorporated into the poor model to improve its performance. The infused model is analyzed theoretically and evaluated empirically on several datasets. Our empirical results showed that CHEER outperformed baselines by 5.60% to 46.80% in terms of the macro-F1 score on multiple physiological datasets.

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“…OTL [36] combines the offline source model with the online target model using a weighting mechanism that is updated with respect to the performance of the source and target models on a sliding window of data in the target domain. Other online TL frameworks [10,17,31] use similar strategies to combine transferred models specifically for classification tasks, and cannot easily be adapted to regressive settings. GOTL [12] extends the OTL weighting mechanism such that online TL can be used for both classification and regression.…”

Section: Related Workmentioning

confidence: 99%

Bi-directional online transfer learning: a framework

et al. 2020

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Transfer learning uses knowledge learnt in source domains to aid predictions in a target domain. When source and target domains are online, they are susceptible to concept drift, which may alter the mapping of knowledge between them. Drifts in online environments can make additional information available in each domain, necessitating continuing knowledge transfer both from source to target and vice versa. To address this, we introduce the Bi-directional Online Transfer Learning (BOTL) framework, which uses knowledge learnt in each online domain to aid predictions in others. We introduce two variants of BOTL that incorporate model culling to minimise negative transfer in frameworks with high volumes of model transfer. We consider the theoretical loss of BOTL, which indicates that BOTL achieves a loss no worse than the underlying concept drift detection algorithm. We evaluate BOTL using two existing concept drift detection algorithms: RePro and ADWIN. Additionally, we present a concept drift detection algorithm, Adaptive Windowing with Proactive drift detection (AWPro), which reduces the computation and communication demands of BOTL. Empirical results are presented using two data stream generators: the drifting hyperplane emulator and the smart home heating simulator, and real-world data predicting Time To Collision (TTC) from vehicle telemetry. The evaluation shows BOTL and its variants outperform the concept drift detection strategies and the existing state-of-the-art online transfer learning technique.

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Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources

Cited by 106 publications

References 22 publications

Online Transfer Learning and Organic Computing for Deep Space Research and Astronomy

Online Transfer Learning and Organic Computing for Deep Space Research and Astronomy

CHEER: Rich Model Helps Poor Model via Knowledge Infusion

Bi-directional online transfer learning: a framework

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