A Survey on Deep Transfer Learning and Beyond

Yu, Fuchao; Xiu, Xianchao; Li, Yunhui

doi:10.3390/math10193619

Cited by 31 publications

(11 citation statements)

References 39 publications

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“…Another intriguing avenue for future research involves relaxing the requirement of local access to all source domain(s) and target domain, similar to federated learning 33 , 34 and some other transfer learning approaches that accommodate pretrained models from source domains. 35 , 36 This collaborative approach could facilitate knowledge transfer from multiple sources without the need to exchange training data.…”

Section: Discussionmentioning

confidence: 99%

Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality

Lee,

Wollstein,

Madu

et al. 2023

Trans. Vis. Sci. Tech.

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Purpose Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. Method We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. Results Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. Conclusions The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. Translational Relevance TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.

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Section: Discussionmentioning

confidence: 99%

Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality

Lee,

Wollstein,

Madu

et al. 2023

Trans. Vis. Sci. Tech.

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“…When analyzing the predictions of the mechanical properties for the realistic geometries, the ANN predictions were not accurate (see Section 3.2 ). Therefore, the ANN was improved by fine-tuning the parameters using the transfer-learning technique ( Tan et al., 2018 ; Yu et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Coronary artery properties in atherosclerosis: A deep learning predictive model

2023

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In this work an Artificial Neural Network (ANN) was developed to help in the diagnosis of plaque vulnerability by predicting the Young modulus of the core (Ecore) and the plaque (Eplaque) of atherosclerotic coronary arteries. A representative in silico database was constructed to train the ANN using Finite Element simulations covering the ranges of mechanical properties present in the bibliography. A statistical analysis to pre-process the data and determine the most influential variables was performed to select the inputs of the ANN. The ANN was based on Multilayer Perceptron architecture and trained using the developed database, resulting in a Mean Squared Error (MSE) in the loss function under 10–7, enabling accurate predictions on the test dataset for Ecore and Eplaque. Finally, the ANN was applied to estimate the mechanical properties of 10,000 realistic plaques, resulting in relative errors lower than 3%.

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“…Deep transfer learning [25] is the use of weight from a related domain to improve model performance and can effectively overcome the problem of insufficient training data. Transfer learning aims to enhance performance by discovering and transferring potential weight from related tasks, and have been successfully applied in many domains [26]. The learning process of transfer learning is shown in Figure 4.…”

Section: Preliminariesmentioning

confidence: 99%

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

Zhang,

Hong,

Ibrahim

et al. 2023

IET Renewable Power Gen

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Distributed photovoltaic (PV) systems often lack adequate measurements due to cost considerations, which makes it very difficult to predict them accurately. Here, an approach is proposed for behind‐the‐meter (BTM) PV power prediction using attention‐LSTM neural network and transfer learning. First, the weather is classified into four types based on the deviation ratio β. Second, the correlation analysis algorithm identifies the weather factors that contribute the most to PV power generation as GHI, DNI, humidity, and temperature. Then, attention‐LSTM pre‐trained model is constructed, in which the LSTM network fully extracts the temporal characteristics of PV power generation, while the attention mechanism enhances the attention to the important information in the input. Finally, a novel BTM PV short‐term power prediction method using transfer learning that the upper layer parameters of the pre‐trained model were frozen, and a small amount of DP‐NB data was used to fine‐tune the model. The proposed methodology has higher prediction accuracy compared with other benchmark methods under four weather types, and the time cost is saved by freezing the parameters of the first layer of the network by 37.2%.

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A Survey on Deep Transfer Learning and Beyond

Cited by 31 publications

References 39 publications

Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality

Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality

Coronary artery properties in atherosclerosis: A deep learning predictive model

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

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