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Publicado en línea: 28 mar 2024
Páginas: 1 - 9
DOI: https://doi.org/10.2478/ijanmc-2024-0001
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© 2024 Ruocheng Ma et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Comparative experimental results of different models
| Accuracy\Model | Category 1 Accuracy | Category 2 Accuracy | Category 3 Accuracy | Category 4 Accuracy | Average accuracy |
| MAML | 82.16% | 72.45% | 81.3% | 85.97% | 80.47% |
| MAML-New | 86.42% | 79.70% | 87.17% | 89.19% | 85.62% |
| ResNet 18-layers | 81.7% | 62.1% | 82.4% | 90.1% | 73.45% |
| LSTM | 81.1% | 68.0% | 80.8% | 80.3% | 77.55% |
Partial network parameter values for MAML and MAML-New
| Parameter | Value | Meaning |
|---|---|---|
| epoch | 600 | Training epochs |
| k | 4 | Number of sample categories |
| k_spt | 20 | Number of support set samples |
| k_qry | 30 | Number of query set samples |
| imgsz | 180 | Dimension of input data |
| imgc | 1 | Number of channels for input data |
| task_num (batch_size) | 16 | Training batch of samples |
| meta_lr | 1e-3 | First gradient update learning rate |
| update_lr | 0.01 | Second gradient update learning rate |