Research Article
Improving the Performance of Deep Learning Model-Based Classification by the Analysis of Local Probability
Table 7
The introduction of the employed acronyms.
| | Num | Acronyms | Introduction |
| | 1 | L-PDL | Our framework, local probability-based deep learning | | 2 | CIFAR-10 | Dataset that is introduced in [15ā17] | | 3 | Mini-ImageNet | Dataset that is introduced in [21ā23] | | 4 | VoVNet-57 | A deep learning model that is introduced in [24] | | 5 | VGG16 | A deep learning model that is introduced in [25] | | 6 | ResNeSt50 | A deep learning model that is introduced in [26] | | 7 | | Presents a sample | | 8 | | Presents a label | | 9 | | Presents the ground truth on | | 10 | Zero20 | Means 20% of the labels have zero samples | | 11 | Zero40 | Means 40% of the labels have zero samples | | 12 | Zero80 | Means 80% of the labels have zero samples | | 13 | L-PDL-joint | Joint cooperation based on our framework, introduced in equation (4) | | 14 | L-PDL-weight | Weighted cooperation based on our framework, introduced in equation (5) | | 15 | Rand (.) | Is the function that outputs random value of probability | | 16 | CC-KL trainable | The existing class-conscious trainable combiner-based KL weights method that is introduced in the work [27] | | 17 | CC-KL trainable with our framework | Our framework on class-conscious trainable combiner-based KL weights method that is introduced in the work [27] |
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