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: creases with the position of the layer in the network. Hence a given input image (cid:126)x is encoded in each layer of the CNN by the ﬁlter responses to that image. A layer with N l distinct ﬁlters has N l feature maps each of size M l , where M l is the height times the width of the feature map. So the responses in a layer l can be stored in a matrix F l ∈ R N l × M l where F lij is the activation of the i th ﬁlter at position j in layer l . To visualise the image information that is encoded at different layers of the hierarchy (Fig 1, content reconstructions) we perform gradient descent on a white noise image to ﬁnd another image that matches the feature responses of the original image. So let (cid:126)p and (cid:126)x be the original image and the image that is generated and P l and F l their respective feature representation in layer l . We then deﬁne the squared-error loss between the two feature representations To generate a texture that matches the style of a given image (Fig 1, style reconstructions), we use gradient descent from a white noise image to ﬁnd another image that matches the style representation of the original image. This is done by minimising the mean-squared distance between the entries of the Gram matrix from the original image and the Gram matrix of the image to be generated. So let (cid:126)a and (cid:126)x be the original image and the image that is generated and A l and G l their respective style representations in layer l . The contribution of that layer to the total loss is then The gradients of E l with respect to the activations in lower layers of the network can be readily computed using standard error back-propagation. The ﬁve style reconstructions in Fig 1 were generated by matching the style representations on layer ‘conv1 1’ (a), ‘conv1 1’ and ‘conv2 1’ (b), ‘conv1 1’, ‘conv2 1’ and ‘conv3 1’ (c), ‘conv1 1’, ‘conv2 1’, ‘conv3 1’ and ‘conv4 1’ (d), ‘conv1 1’, ‘conv2 1’, ‘conv3 1’, ‘conv4 1’ and ‘conv5 1’ (e). To generate the images that mix the content of a photograph with the style of a painting (Fig 2) we jointly minimise the distance of a white noise image from the content representation 11

Question: What is the benefit of using "white noise" instead of any other noise like Salt-and-pepper or Gaussian noise?
The authors use the white noise image as a starting point for the loss function to turn it into a combination of given images. And the results suggest that it works well. However, the reasons behind using the white noise image and its effectiveness are not discussed, thus it is difficult to answer the question.