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CPICANN Pretrained Models Repository
This repository contains the pretrained models of the method described in the CPICANN paper, available at
GitHub.
Ablation Study on Self-Attention Configuration
In Table 1 below, we present the results of an ablation study on self-attention configuration:
| No. | Model Configuration | Trainable Parameters | Accuracy on Validation Set (%) |
|---|
| 1 | ED: 128, HN: 8, SL: 4 | 13,725,793 | 86.16 |
| 2 | ED: 128, HN: 8, SL: 6 （CPICANN） | 14,385,505 | 87.50 |
| 3 | ED: 128, HN: 8, SL: 8 | 15,045,217 | 86.94 |
| 4 | ED: 256, HN: 8, SL: 6 | 17,243,873 | 85.51 |
| 5 | ED: 384, HN: 8, SL: 6 | 20,872,161 | 86.14 |
| 6 | ED: 128, HN: 4, SL: 6 | 14,385,505 | 86.43 |
| 7 | ED: 384, HN: 6, SL: 6 | 20,872,161 | 85.78 |
Based on the validation set accuracy, the self-attention module is optimized within the following ranges: self-attention layers of 4, 6, or 8; embedding dimensions of 128, 256, or 384; and head numbers of 4, 6, or 8. The results are detailed in Table S1, with the notations of ED for embedding dimensions, HN for head number, and SL for the number of self-attention layers. The ablation study identifies the optimal configuration of CPICANN as ED: 128, HN: 8, SL: 6.
CNNonly and ATTENTIONonly Models
Two additional models, CNNonly and ATTENTIONonly, isolate the CNN and attention parts of CPICANN, respectively.
Datasets Tested
CPICANN is evaluated on four distinguished datasets, denoted as D1, D2, D3, and D4, with the following characteristics:
- D1: 0% background ratio and Gaussian noise (σ=0.25) (v chosen in paper)
- D2: 3% background ratio and Gaussian noise (σ=0.25)
- D3: 0% background ratio and Gaussian noise (σ=1)
- D4: 0% background ratio and Gaussian noise (σ=3)
Contribution and suggestions are always welcome. You can also contact the authors for research collaboration.
