| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T447 |
0-93 |
Sentence |
denotes |
The training strategies and hyper-parameters of the classification framework were as follows. |
| T448 |
94-272 |
Sentence |
denotes |
We adopted a knowledge distillation method (Fig. 7) to train the CNNCF as a student network with one stage I block and one stage II block, each of which contained two ResBlock-A. |
| T449 |
273-477 |
Sentence |
denotes |
Four teacher networks (the hyper-parameters are provided in Supplementary Table 21) with the proposed blocks were trained on the train-val part of each sub-data set using a 5-fold cross-validation method. |
| T450 |
478-547 |
Sentence |
denotes |
All networks were initialized using the Xavier initialization method. |
| T451 |
548-626 |
Sentence |
denotes |
The initial learning rate was 0.01, and the optimization function was the SGD. |
| T452 |
627-737 |
Sentence |
denotes |
The CNNCF was trained using the image data and the label, as well as the fused output of the teacher networks. |
| T453 |
738-860 |
Sentence |
denotes |
The comparison of RT-PCR test results using throat specimen and the CNNCF results were provided in Supplementary Table 22. |
| T454 |
861-938 |
Sentence |
denotes |
Supplementary Fig. 20 shows the details of the knowledge distillation method. |
| T455 |
939-1055 |
Sentence |
denotes |
The definitions and details of the five evaluation indicators used in this study were given in Supplementary Note 2. |
| T456 |
1056-1155 |
Sentence |
denotes |
Fig. 7 Knowledge distillation consisting of multiple teacher networks and a target student network. |
| T457 |
1156-1256 |
Sentence |
denotes |
The knowledge is transferred from the teacher networks to the student network using a loss function. |
