employed 9×9 convolution kernels to replace part of the 3×3 convolution kernels
时间: 2024-05-29 13:08:26 浏览: 13
in their neural network architecture.
This change was made in order to increase the receptive field of the network, allowing it to better capture spatial relationships between pixels in an image. By using larger kernels, the network is able to consider a wider area of the image when making predictions, leading to improved accuracy.
However, using larger kernels also increases the computational cost of the network, as more parameters need to be learned. Therefore, the researchers used a combination of 9×9 and 3×3 kernels, with the larger kernels being used in the deeper layers of the network where the receptive field is larger, and the smaller kernels being used in the shallower layers where the receptive field is smaller.
Overall, this approach resulted in a significant improvement in performance on benchmark datasets, demonstrating the effectiveness of using larger kernels in certain parts of a convolutional neural network.
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