Transparency and Interpretability for Learned Representations of Artificial Neural Networks

Meyes, Richard

doi:10.1007/978-3-658-40004-0

Cited by 16 publications

(18 citation statements)

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“…An ablation study, commonly applied in neuroscience research, is employed for artificial neural network performance analysis [44]. The objective is to investigate the change in classification accuracy with different numbers of neural network layers or different features.…”

Section: Ablation Study On Layer Depthmentioning

confidence: 99%

EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

2021

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Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a crucial non-invasive application in brain–computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods. Approach. The proposed CNN model, namely EEG-inception, is built on the backbone of the inception-time network, which has showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing. Furthermore, this paper proposes a novel data augmentation method for EEG signals to enhance the accuracy, at least by 3%, and reduce overfitting with limited BCI datasets. Main results. The proposed model outperforms all state-of-the-art methods by achieving the average accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes) and 2b datasets (binary-classes), respectively. Furthermore, it takes less than 0.025 s to test a sample suitable for real-time processing. Moreover, the classification standard deviation for nine different subjects achieves the lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which validates that the proposed method is highly robust. Significance. From the experiment results, it can be inferred that the EEG-inception network exhibits a strong potential as a subject-independent classifier for EEG-based MI tasks.

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Section: Ablation Study On Layer Depthmentioning

confidence: 99%

EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

2021

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“…Table 4 shows how the results are affected by altering different building blocks of our model 40 . We select the model with the best validation Dice score (mean of GGO and high opacity) for the final evaluation.…”

Section: Resultsmentioning

confidence: 99%

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks

et al. 2022

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. Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of ; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95–0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.

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“…In contemporary machine learning involving deep models [31,32], ablation analysis is frequently used to identify the role and/or importance of different features or neuron groups within a neural network. By selectively removing certain features or neurons and assessing how this affects the model's performance, researchers can identify which components are critical to the model's predictive power and which are less important.…”

Section: Methodsmentioning

confidence: 99%

Energy Demand in AR Applications—A Reverse Ablation Study of the HoloLens 2 Device

Skurowski,

Myszor,

Paszkuta

et al. 2024

Energies

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This study presents a comprehensive analysis of power consumption in the context of AR applications on the HoloLens 2. As an ablation study, the research systematically dissects various aspects, shedding light on both hardware and software components. The power consumption was examined in diverse scenarios, ranging from the device at idle to running AR applications of increasing complexity. Overall, the study provides insights into the power management aspects associated with Hololens 2 AR devices and applications, offering cues for optimizing energy efficiency and enhancing the user experience. The findings confirm that display-related factors, such as the screen brightness and content complexity, significantly impact power consumption. However, the results reveal that power usage does not scale linearly with the number of objects in an AR scene due to overhead factors and object occlusion. Furthermore, usage scenarios such as lighting conditions and large/minor user motions are analyzed. Moreover, the influence of different development platforms, Unity and Unreal, on power consumption is discussed, highlighting their respective contributions to energy usage. These findings might be useful for researchers, developers, and users of AR technology.

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Transparency and Interpretability for Learned Representations of Artificial Neural Networks

Cited by 16 publications

References 0 publications

EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks

Energy Demand in AR Applications—A Reverse Ablation Study of the HoloLens 2 Device

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