How does SEGTRAN improve the accuracy of face recognition systems compared to traditional convolutional neural networks?
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SEGTRAN improves the accuracy of face recognition systems compared to traditional convolutional neural networks by using several improvement techniques . These techniques include adopting a less computationally costly approach, transfer learning, and hyper-parameter fine-tuning . By implementing these techniques, the base MobileNet-V1 model's Top-1 accuracy of 70.6% and Top-5 accuracy of 89.5% have been improved . The training accuracy has been increased to 95%, and accuracies of 96.4%, 98.0%, and 99.1% have been achieved on different face recognition datasets . This improvement in accuracy demonstrates the effectiveness of SEGTRAN and highlights the need for further research into improvement techniques for convolutional neural networks .
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20 Nov 2022 | The provided paper does not mention SEGTRAN or any technique specifically called SEGTRAN. |
20 Nov 2022 | The provided paper does not mention SEGTRAN or any technique specifically called SEGTRAN. |
21 Dec 2022 | The provided paper does not mention SEGTRAN or any improvement in accuracy compared to traditional convolutional neural networks. |
21 Dec 2022 | The provided paper does not mention SEGTRAN or any improvement in accuracy compared to traditional convolutional neural networks. |
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