RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes e...

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Bibliographic Details
Published in:The Journal of supercomputing 2023, Vol.79 (10), p.11355-11386
Main Authors: Ahmed, Usman, Lin, Jerry Chun-Wei
Format: Article
Language:English
Subjects:
Accuracy
Compilers
Computer Science
Datasets
Deep learning
Entropy
Interpreters
Machine learning
Medical imaging
Neural networks
Processor Architectures
Programming Languages
Robustness
Sorting algorithms
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Summary:This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes entropy values and a non-dominant sorting algorithm to identify the candidate with the highest entropy value from the dataset. This is followed by merging the training set with adversarial samples, where a mini-batch of the merged dataset is used to update the dense network parameters. This method can improve the performance of machine learning models, categorization of radiographic images, risk of misdiagnosis in medical imaging, and accuracy of medical diagnoses. To evaluate the efficacy of the proposed model, two datasets, MNIST and COVID, were used with pixel values and without transfer learning. The results showed an increase of accuracy from 0.85 to 0.88 for MNIST and from 0.83 to 0.85 for COVID, which suggests that the model successfully classified images from both datasets without using transfer learning techniques.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05087-5