A Thread-Saving Schedule with Graph Analysis for Parallel Deep Learning Applications on Embedded Systems

Huang, Shu; Wang, Jihe; Wang, Danghui; Zhang, Meng; You, Hongjun

doi:10.1109/smartcloud.2018.00026

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In these days, deep learning models have been proved efficient in many applications [1][2][3][4][5][6][7][8]. Generally, the performance of a deep learning-based classification model depends on the captured features [9][10][11]. When using a deep learning mode for the classification, the probability of each object is outputted.…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Deep Learning Model‐Based Classification by the Analysis of Local Probability

Jin

Jiao

et al. 2021

Complexity

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Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L-PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Improving the Performance of Deep Learning Model‐Based Classification by the Analysis of Local Probability

Jin

Jiao

et al. 2021

Complexity

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“…When utilizing the deep learning models for the classification, the performance of models is related to the captured feature. To get higher performance for the classification tasks, the structure should be well designed and the hyperparameters should be well tuned [6][7][8]. When the training set contains the samples that belong to different domains, the diverse features may lower the performance of trained models [9,10].…”

Section: Introductionmentioning

confidence: 99%

Solving the Feature Diversity Problem Based on Multi-Model Scheme

Jin¹,

Zhao²,

Pei³

et al. 2021

Journal on Artificial Intelligence

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Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion method on the outputs of the selected models to get more accurate results. The experimental results show that our framework achieved higher accuracy than the existing methods. Our framework can be an option for the deep learning system to increase the classification accuracy.

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Improving the performance of deep learning-based classification when a sample has various appearances

Jin

Jiao

Wang

et al. 2022

Journal of Experimental & Theoretical Artificial Intelligen

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A Thread-Saving Schedule with Graph Analysis for Parallel Deep Learning Applications on Embedded Systems

Cited by 3 publications

References 12 publications

Improving the Performance of Deep Learning Model‐Based Classification by the Analysis of Local Probability

Improving the Performance of Deep Learning Model‐Based Classification by the Analysis of Local Probability

Solving the Feature Diversity Problem Based on Multi-Model Scheme

Improving the performance of deep learning-based classification when a sample has various appearances

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