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: We notice that state-of-the-art basic architectures such as Xception [3] and ResNeXt [40] become less efficient in extremely small networks because of the costly dense 1\times 1 convolutions. We propose using pointwise group convolutions to reduce computation complexity of 1\times 1 convolutions. To overcome the side effects brought by group convolutions, we come up with a novel channel shuffle operation to help the information flowing across feature channels. Based on the two techniques, we build a highly efficient architecture called ShuffleNet. Compared with popular structures like  [30, 9, 40], for a given computation complexity budget, our ShuffleNet allows more feature map channels, which helps to encode more information and is especially critical to the performance of very small networks. From the results, we see that models with group convolutions (g>1) consistently perform better than the counterparts without pointwise group convolutions (g=1). Smaller models tend to benefit more from groups. For example, for ShuffleNet 1\times the best entry (g=8) is 1.2% better than the counterpart, while for ShuffleNet 0.5\times and 0.25\times the gaps become 3.5% and 4.4% respectively. Note that group convolution allows more feature map channels for a given complexity constraint, so we hypothesize that the performance gain comes from wider feature maps which help to encode more information. In addition, a smaller network involves thinner feature maps, meaning it benefits more from enlarged feature maps. The concept of group convolution, which was first introduced in AlexNet [21] for distributing the model over two GPUs, has been well demonstrated its effectiveness in ResNeXt [40]. Depthwise separable convolution proposed in Xception [3] generalizes the ideas of separable convolutions in Inception series [34, 32]. Recently, MobileNet [12] utilizes the depthwise separable convolutions and gains state-of-the-art results among lightweight models. Our work generalizes group convolution and depthwise separable convolution in a novel form. Modern convolutional neural networks [30, 33, 34, 32, 9, 10] usually consist of repeated building blocks with the same structure. Among them, state-of-the-art networks such as Xception [3] and ResNeXt [40] introduce efficient depthwise separable convolutions or group convolutions into the building blocks to strike an excellent trade-off between representation capability and computational cost. However, we notice that both designs do not fully take the 1\times 1 convolutions (also called pointwise convolutions in  [12]) into account, which require considerable complexity. For example, in ResNeXt [40] only 3\times 3 layers are equipped with group convolutions. As a result, for each residual unit in ResNeXt the pointwise convolutions occupy 93.4% multiplication-adds (cardinality = 32 as suggested in  [40]). In tiny networks, expensive pointwise convolutions result in limited number of channels to meet the complexity constraint, which might significantly damage the accuracy. To address the issue, a straightforward solution is to apply channel sparse connections, for example group convolutions, also on 1\times 1 layers. By ensuring that each convolution operates only on the corresponding input channel group, group convolution significantly reduces computation cost. However, if multiple group convolutions stack together, there is one side effect: outputs from a certain channel are only derived from a small fraction of input channels. Fig 1 (a) illustrates a situation of two stacked group convolution layers. It is clear that outputs from a certain group only relate to the inputs within the group. This property blocks information flow between channel groups and weakens representation.

Question: How pointwise group convolutions is different from 1x1 convolutions?
The group convolution divides the channels into groups and applies the convolution only within the groups, thus reducing the computational complexity of 1x1 convolutions. However, when several group convolutions are stacked together it may block the information flow and weaken the representation.