CNN segmentation validation loss throughout training.

Decoding trends of CNN-1 compared with CCA-KNN for the individual subjects.

No-Sang Kwak (3772630)
Klaus-Robert Müller (52696)
Seong-Whan Lee (391862)

February 2017

Decoding trends of CNN1 compared with CCA-KNN by the number of training data in static SSVEP (a) ...

Comparison of predicted segmentation vs. ground truth for a Mask R-CNN model (object-based tile positioning + object integrity constraint, 100% training data) applied with a prediction score threshold of 0.98 on previously unseen test data.

Michael Kloster (7272371)
Andrea M. Burfeid-Castellanos (10731527)
Daniel Langenkämper (5983205)
Tim W. Nattkemper (9116711)
Bánk Beszteri (122110)

February 2023

Black arrows indicate substantial changes in comparison to Fig 9 (prediction score threshold of 0.9)...

Loss curves.

Matthijs Pals (8952902)
Jakob H. Macke (4487488)
Omri Barak (379217)

February 2024

Loss over epochs for three models, each trained with LFP data from a separate rat. An epoch denotes ...

CNN segmentation validation loss throughout training.

Alex J. Allphin (17102503)
Ali Mahzarnia (12368498)
Darin P. Clark (7406084)
Yi Qi (181890)
Zay Y. Han (17102506)
Prajwal Bhandari (15178474)
Ketan B. Ghaghada (327508)
Alexandra Badea (352052)
Cristian T. Badea (7406093)

October 2023

We show here the validation loss (normalized from 0 to 1) as a function of epoch number throughout C...

Diagram of training (red) and validation (black) status: Evolving convergence loss with growing number of training epochs.

Alexander Kihm (5400674)
Lars Kaestner (175253)
Christian Wagner (5599)
Stephan Quint (5400671)

June 2018

We set a maximum of ten epochs since a prolongation to more training epochs yields no gain in per...

CNN segmentation performance metrics for training, validation, and test sets.

Alex J. Allphin (17102503)
Ali Mahzarnia (12368498)
Darin P. Clark (7406084)
Yi Qi (181890)
Zay Y. Han (17102506)
Prajwal Bhandari (15178474)
Ketan B. Ghaghada (327508)
Alexandra Badea (352052)
Cristian T. Badea (7406093)

October 2023

CNN segmentation performance metrics for training, validation, and test sets.</p

Performance on object detection during the training process.

Osvaldo Matias Velarde (17373753)
Hernán A. Makse (8763285)
Lucas C. Parra (12840692)

November 2023

The results here are on the validation set. Recurrent CNNs (a-d) were used as backbones in Faster R-...

Fig 7 -

Galen Richardson (17419550)
Anders Knudby (492541)
Wenjun Chen (176385)
Michael Sawada (58007)
Julie Lovitt (17419553)
Liming He (419765)
Leila Yousefizadeh Naeni (17419556)

November 2023

A. Visual representation of the CNN model using Net2Vis [44], B. training and validation loss curves...

Typical segmentation results of Mask R-CNN models trained on 100% training data, with a prediction score threshold of 0.95.

Michael Kloster (7272371)
Andrea M. Burfeid-Castellanos (10731527)
Daniel Langenkämper (5983205)
Tim W. Nattkemper (9116711)
Bánk Beszteri (122110)

February 2023

a) Input data, b) segmentation result for fixed-stride training data, c) segmentation result for obj...

UR-65 CNN CIFAR Image Identification

April 2021

Reducing the learning rate of a CNN can positively affect the validation accuracy of a machine learn...

Per epochs loss estimation for deep learning models.

Shengjun Ji (6394568)

January 2024

In these figures, ‘val’ indicates validation, so ‘val_loss’ represents the loss on the validation se...

Learning curve of the Ashcroft fibrosis-CNN.

Fabian Heinemann (2043625)
Gerald Birk (680814)
Tanja Schoenberger (5680469)
Birgit Stierstorfer (3687724)

August 2018

The learning curve shows the accuracy of the CNN on the training and validation data vs. the epochs ...

CNN test results under three different training methods (a.lr = 0.01; b.lr = 0.001).

Yan Feng (128912)

October 2023

CNN test results under three different training methods (a.lr = 0.01; b.lr = 0.001).</p

Accuracy in classification task.

Osvaldo Matias Velarde (17373753)
Hernán A. Makse (8763285)
Lucas C. Parra (12840692)

November 2023

Recurrent CNNs (a-c) were used as feature extractors in the classification task. (a, c) Feedforwards...

Performance comparison of CNN models with different region sizes and other baseline models.

Hong-Jie Dai (36448)
Jitendra Jonnagaddala (5859893)

October 2018

Performance comparison of CNN models with different region sizes and other baseline models.</p

Decoding trends of CNN-1 compared with CCA-KNN for the individual subjects.

No-Sang Kwak (3772630)
Klaus-Robert Müller (52696)
Seong-Whan Lee (391862)

February 2017

Decoding trends of CNN1 compared with CCA-KNN by the number of training data in static SSVEP (a) ...

Comparison of predicted segmentation vs. ground truth for a Mask R-CNN model (object-based tile positioning + object integrity constraint, 100% training data) applied with a prediction score threshold of 0.98 on previously unseen test data.

Michael Kloster (7272371)
Andrea M. Burfeid-Castellanos (10731527)
Daniel Langenkämper (5983205)
Tim W. Nattkemper (9116711)
Bánk Beszteri (122110)

February 2023

Black arrows indicate substantial changes in comparison to Fig 9 (prediction score threshold of 0.9)...

Loss curves.

Matthijs Pals (8952902)
Jakob H. Macke (4487488)
Omri Barak (379217)

February 2024

Loss over epochs for three models, each trained with LFP data from a separate rat. An epoch denotes ...

CNN segmentation validation loss throughout training.

Alex J. Allphin (17102503)
Ali Mahzarnia (12368498)
Darin P. Clark (7406084)
Yi Qi (181890)
Zay Y. Han (17102506)
Prajwal Bhandari (15178474)
Ketan B. Ghaghada (327508)
Alexandra Badea (352052)
Cristian T. Badea (7406093)

October 2023

We show here the validation loss (normalized from 0 to 1) as a function of epoch number throughout C...

Diagram of training (red) and validation (black) status: Evolving convergence loss with growing number of training epochs.

Alexander Kihm (5400674)
Lars Kaestner (175253)
Christian Wagner (5599)
Stephan Quint (5400671)

June 2018

We set a maximum of ten epochs since a prolongation to more training epochs yields no gain in per...

CNN segmentation performance metrics for training, validation, and test sets.

Alex J. Allphin (17102503)
Ali Mahzarnia (12368498)
Darin P. Clark (7406084)
Yi Qi (181890)
Zay Y. Han (17102506)
Prajwal Bhandari (15178474)
Ketan B. Ghaghada (327508)
Alexandra Badea (352052)
Cristian T. Badea (7406093)

October 2023

CNN segmentation performance metrics for training, validation, and test sets.</p

Performance on object detection during the training process.

Osvaldo Matias Velarde (17373753)
Hernán A. Makse (8763285)
Lucas C. Parra (12840692)

November 2023

The results here are on the validation set. Recurrent CNNs (a-d) were used as backbones in Faster R-...

Fig 7 -

Galen Richardson (17419550)
Anders Knudby (492541)
Wenjun Chen (176385)
Michael Sawada (58007)
Julie Lovitt (17419553)
Liming He (419765)
Leila Yousefizadeh Naeni (17419556)

November 2023

A. Visual representation of the CNN model using Net2Vis [44], B. training and validation loss curves...

Typical segmentation results of Mask R-CNN models trained on 100% training data, with a prediction score threshold of 0.95.

Michael Kloster (7272371)
Andrea M. Burfeid-Castellanos (10731527)
Daniel Langenkämper (5983205)
Tim W. Nattkemper (9116711)
Bánk Beszteri (122110)

February 2023

a) Input data, b) segmentation result for fixed-stride training data, c) segmentation result for obj...

UR-65 CNN CIFAR Image Identification

April 2021

Reducing the learning rate of a CNN can positively affect the validation accuracy of a machine learn...

Per epochs loss estimation for deep learning models.

Shengjun Ji (6394568)

January 2024

In these figures, ‘val’ indicates validation, so ‘val_loss’ represents the loss on the validation se...

Learning curve of the Ashcroft fibrosis-CNN.

Fabian Heinemann (2043625)
Gerald Birk (680814)
Tanja Schoenberger (5680469)
Birgit Stierstorfer (3687724)

August 2018

The learning curve shows the accuracy of the CNN on the training and validation data vs. the epochs ...

CNN test results under three different training methods (a.lr = 0.01; b.lr = 0.001).

Yan Feng (128912)

October 2023

CNN test results under three different training methods (a.lr = 0.01; b.lr = 0.001).</p

Accuracy in classification task.

Osvaldo Matias Velarde (17373753)
Hernán A. Makse (8763285)
Lucas C. Parra (12840692)

November 2023

Recurrent CNNs (a-c) were used as feature extractors in the classification task. (a, c) Feedforwards...

Performance comparison of CNN models with different region sizes and other baseline models.

Hong-Jie Dai (36448)
Jitendra Jonnagaddala (5859893)

October 2018

Performance comparison of CNN models with different region sizes and other baseline models.</p

Decoding trends of CNN-1 compared with CCA-KNN for the individual subjects.

No-Sang Kwak (3772630)
Klaus-Robert Müller (52696)
Seong-Whan Lee (391862)

February 2017

Decoding trends of CNN1 compared with CCA-KNN by the number of training data in static SSVEP (a) ...

Comparison of predicted segmentation vs. ground truth for a Mask R-CNN model (object-based tile positioning + object integrity constraint, 100% training data) applied with a prediction score threshold of 0.98 on previously unseen test data.

Michael Kloster (7272371)
Andrea M. Burfeid-Castellanos (10731527)
Daniel Langenkämper (5983205)
Tim W. Nattkemper (9116711)
Bánk Beszteri (122110)

February 2023

Black arrows indicate substantial changes in comparison to Fig 9 (prediction score threshold of 0.9)...

Loss curves.

Matthijs Pals (8952902)
Jakob H. Macke (4487488)
Omri Barak (379217)

February 2024

Loss over epochs for three models, each trained with LFP data from a separate rat. An epoch denotes ...

CNN segmentation validation loss throughout training.

Abstract

Extracted data

CNN segmentation validation loss throughout training.

Abstract

Extracted data

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