Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning

Konno, Tomohiko; Iwazume, Michiaki

doi:10.48550/arxiv.1807.06540

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…On the other hand, the second category employs deep learning techniques for data augmentation, which includes increasing the feature space [24], adversarial training [25], and generative adversarial networks (GANs). GANs have gained significant popularity in the clinical field since their introduction in 2014 [26].…”

Section: Data Augmentationmentioning

confidence: 99%

“…Although these methods are known to suffer from some drawbacks and often low performance gains that do not justify their use in some domains, traditional algebraic transformation methods also remain widespread in the medical field: in [31], the authors analyze five different methods belonging to the family of direct image manipulation for prostate cancer detection. The second class makes use of deep learning to perform data augmentation: examples include the feature space augmentation [24] , adversarial training [25] , and generative adversarial networks (GAN) [26] methods. The latter has been one of the most widely used approaches in clinical settings since its introduction in 2014.…”

Section: Data Augmentationmentioning

confidence: 99%

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CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

et al. 2023

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Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper.

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Section: Data Augmentationmentioning

confidence: 99%

Section: Data Augmentationmentioning

confidence: 99%

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The data transformation approach includes geometric transformation [31], color space transformation [32]- [34], random erasing [35]- [37], adversarial training [38]- [41] and style transfer [42]- [45]. The resampling technique lays particular emphasis on new instance composition, such as image mixup [46]- [48], feature space enhancement [49], [50] and generative adversarial network (GAN) [16]. Geometric transformation can acquire nice performance, such as image flip, crop, rotation, translation, and noise injection [51].…”

Section: Data Augmentationmentioning

confidence: 99%

Physical Knowledge-Enhanced Deep Neural Network for Sea Surface Temperature Prediction

Meng

Gao

Rigall

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting Sea Surface Temperature (SST). Recently, the advances in earth observation technologies have yielded a monumental growth of data. Consequently, it is imperative to explore ways in which to improve and supplement numerical models utilizing the everincreasing amounts of historical observational data. To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data is then fed into the pre-trained model to generate physics-enhanced data, which can then be used for SST prediction. Experimental results demonstrate that the proposed method considerably enhances SST prediction performance when compared to several state-ofthe-art baselines.

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“…On this foundation, Liu et al [41] proposed the feature migration network (FATTEN) for describing motion trajectory changes caused by object pose changes, which applied the migration learning problem of objects with the pose. Konno et al [42] manipulated the modularity of the neural network by separating and refining the individual network layers after training the model, resulting in a 13% improvement in the accuracy of the model. However, the feature space expansion method is hard to interpret vector data.…”

Section: Data Augmentationmentioning

confidence: 99%

Crude Oil Leakage Detection Based on DA‐SR Framework

Gong

Gao

Yuan

et al. 2022

Advcd Theory and Sims

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Crude oil leakage is a security issue that needs to be avoided in many production areas such as oil fields and substations. However, crude oil leakage image data is often difficult to obtain due to security and privacy issues in the working area. And shadow interference is also a challenge for oil leakage detection tasks. This paper proposes a crude oil leakage detection method based on the DA‐SR framework. The framework consists of two parts: the data augmentation module and shadow removal module. High‐quality synthetic oil leakage images are generated using the cycle‐consistent adversarial networks (CycleGAN), and further process the synthetic images by a T‐CutMix sample processing method. To solve the problem of shadow interference, this paper uses the FlocalLoss function to calculate the confidence loss based on the YOLOv4 detection network and a hard sample retraining (HSR) algorithm to enhance the images with shadows. The experiments demonstrate that the combination of original and synthetic images when training the model can improve the performance of oil leakage detection. Finally, it is also shown that the detector built from the framework can effectively reduce the false detection of shadows.

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Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning

Cited by 4 publications

References 19 publications

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

Physical Knowledge-Enhanced Deep Neural Network for Sea Surface Temperature Prediction

Crude Oil Leakage Detection Based on DA‐SR Framework

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