You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Deeper networks have greater discriminative power due to the additional non-linearities and better quality of local optima (Choromanska et al. (2015)). However, convolutions with 3D kernels are computationally expensive in comparison to the 2D variants, which hampers the addition of more layers. Additionally, 3D architectures have a larger number of trainable parameters, with each layer adding C_{l}C_{l-1}\prod_{i=\{x,y,z\}}{\bm{\kappa}_{l}^{(i)}} weights to the model. C_{l} is the number of FMs in layer l and \bm{\kappa}_{l}^{\{x,y,z\}} the size of its kernel in the respective spatial dimension. Overall this makes the network increasingly prone to over-fitting. In order to build a deeper 3D architecture, we adopt the sole use of small 3^{3} kernels that are faster to convolve with and contain less weights. This design approach was previously found beneficial for classification of natural images (Simonyan and Zisserman (2014)) but its effect is even more drastic on 3D networks. When compared to common kernel choices of 5^{3} (Zikic et al. (2014); Urban et al. (2014); Prasoon et al. (2013)) and in our baseline CNN, the smaller 3^{3} kernels reduce the element-wise multiplications by a factor of approximately 5^{3}/3^{3}\approx 4.6 while reducing the number of trainable parameters by the same factor. Thus deeper network variants that are implicitly regularised and more efficient can be designed by simply replacing each layer of common architectures with more layers that use smaller kernels (Fig. 4).

Question: Why does a deeper network with smaller kernel size have better performances ?
Deeper networks exhibit better performance as they introduce more non-linearities and converge towards better local optima. But, adding more layers increase both computation time and the number of parameters. This could cause the network to be prone to overfitting. Therefore, kernel sizes were reduced such that the number of parameters were similar to the original shallow networks, helping with generalization by reducing overfitting while still receiving the positive effect of having more layers.