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: If we allow group convolution to obtain input data from different groups (as shown in Fig 1 (b)), the input and output channels will be fully related. Specifically, for the feature map generated from the previous group layer, we can first divide the channels in each group into several subgroups, then feed each group in the next layer with different subgroups. This can be efficiently and elegantly implemented by a channel shuffle operation (Fig 1 (c)): suppose a convolutional layer with g groups whose output has g\times n channels; we first reshape the output channel dimension into (g,n), transposing and then flattening it back as the input of next layer. Note that the operation still takes effect even if the two convolutions have different numbers of groups. Moreover, channel shuffle is also differentiable, which means it can be embedded into network structures for end-to-end training. 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. Table 2 also shows that for some models (e.g. ShuffleNet 0.5\times) when group numbers become relatively large (e.g. g=8), the classification score saturates or even drops. With an increase in group number (thus wider feature maps), input channels for each convolutional filter become fewer, which may harm representation capability. Interestingly, we also notice that for smaller models such as ShuffleNet 0.25\times larger group numbers tend to better results consistently, which suggests wider feature maps bring more benefits for smaller models. The purpose of shuffle operation is to enable cross-group information flow for multiple group convolution layers. Table 3 compares the performance of ShuffleNet structures (group number is set to 3 or 8 for instance) with/without channel shuffle. The evaluations are performed under three different scales of complexity. It is clear that channel shuffle consistently boosts classification scores for different settings. Especially, when group number is relatively large (e.g. g=8), models with channel shuffle outperform the counterparts by a significant margin, which shows the importance of cross-group information interchange. 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: Why it is possible to say that multiple group convolutional layers works efficiently without weakening representation?
It is clearly stated in the paper that having group convolutions is a trade-off between representative capability and the computational cost of the model. The ShuffleNet allows stacking multiple group convolutions with an appropriate number of groups because of channel shuffle and it is empirically shown in the paper. However, it is also noted that having too many groups might sometimes damage the performance. Thus, multiple group convolutions work efficiently only when the number of groups is chosen carefully and channel shuffle is used.