Rank level fusion of multimodal biometrics using genetic algorithm

Ahmad, Shadab; Pal, Rajarshi; Ganivada, Avatharam

doi:10.1007/s11042-022-12688-4

Cited by 6 publications

(1 citation statement)

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“…To combine various metrics, existing approaches often employ weighted fusion [44] or rank fusion [45] to directly rank the augmented datasets. However, these methods are not suitable in this case because the metrics from different dimensions cannot be directly compared, and a comparison between the augmented training set and the original training set is required.…”

Section: Results Statisticsmentioning

confidence: 99%

ADQE: Obtain Better Deep Learning Models by Evaluating the Augmented Data Quality Using Information Entropy

Cui,

Li,

Xie

et al. 2023

Electronics

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Data augmentation, as a common technique in deep learning training, is primarily used to mitigate overfitting problems, especially with small-scale datasets. However, it is difficult for us to evaluate whether the augmented dataset truly benefits the performance of the model. If the training model is relied upon in each case to validate the quality of the data augmentation and the dataset, it will take a lot of time and resources. This article proposes a simple and practical approach to evaluate the quality of data augmentation for image classification tasks, enriching the theoretical research on data augmentation quality evaluation. Based on the information entropy, multiple dimensional metrics for data quality augmentation are established, including diversity, class balance, and task relevance. Additionally, a comprehensive data augmentation quality fusion metric is proposed. Experimental results on the CIFAR-10 and CUB-200 datasets show that our method maintains optimal performance in a variety of scenarios. The cosine similarity between the score of our method and the precision of model is up to 99.9%. A rigorous evaluation of data augmentation quality is necessary to guide the improvement of DL model performance. The quality standards and evaluation defined in this article can be utilized by researchers to train high-performance DL models in situations where data are limited.

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Section: Results Statisticsmentioning

confidence: 99%