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. In ShuffleNet units, group number g controls the connection sparsity of pointwise convolutions. Table 1 explores different group numbers and we adapt the output channels to ensure overall computation cost roughly unchanged (\sim140 MFLOPs). Obviously, larger group numbers result in more output channels (thus more convolutional filters) for a given complexity constraint, which helps to encode more information, though it might also lead to degradation for an individual convolutional filter due to limited corresponding input channels. In Sec 4.1.1 we will study the impact of this number subject to different computational constrains. 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.

Question: How channel shuffle operation works for two groups?
In the case of channel shuffle operation for two groups, each group is divided into two and shuffled so each new group has a subgroup from both old groups. For example, |A|B| -> |aa|bb| -> |ab|ab|. In terms of performance, two groups seem to work consistently better than the single group case and consistently worse than having more than 2 groups.