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The reconstruction performance using different models.

Journal: Frontiers in Computational Neuroscience

Article Title: U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

doi: 10.3389/fncom.2024.1387004

Figure Lengend Snippet: The reconstruction performance using different models.

Article Snippet: Measuring the similarity between generated and empirical time-series data is a crucial step in evaluating the performance of the proposed model. Three metrics can be used for this purpose, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination of the prediction (R2) (Ma et al., ), and dynamic time warping (DTW) (Philips et al., ).

Techniques:

The reconstruction performance using different models for different noise levels.

Journal: Frontiers in Computational Neuroscience

Article Title: U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

doi: 10.3389/fncom.2024.1387004

Figure Lengend Snippet: The reconstruction performance using different models for different noise levels.

Article Snippet: Measuring the similarity between generated and empirical time-series data is a crucial step in evaluating the performance of the proposed model. Three metrics can be used for this purpose, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination of the prediction (R2) (Ma et al., ), and dynamic time warping (DTW) (Philips et al., ).

Techniques:

Influence of different model's module on the reconstruction performance.

Journal: Frontiers in Computational Neuroscience

Article Title: U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

doi: 10.3389/fncom.2024.1387004

Figure Lengend Snippet: Influence of different model's module on the reconstruction performance.

Article Snippet: Measuring the similarity between generated and empirical time-series data is a crucial step in evaluating the performance of the proposed model. Three metrics can be used for this purpose, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination of the prediction (R2) (Ma et al., ), and dynamic time warping (DTW) (Philips et al., ).

Techniques: