  Recently over-smoothing phenomenon of Transformer-based models is observed in
both vision and language fields. However, no existing work has delved deeper to
further investigate the main cause of this phenomenon. In this work, we make
the attempt to analyze the over-smoothing problem from the perspective of
graph, where such problem was first discovered and explored. Intuitively, the
self-attention matrix can be seen as a normalized adjacent matrix of a
corresponding graph. Based on the above connection, we provide some theoretical
analysis and find that layer normalization plays a key role in the
over-smoothing issue of Transformer-based models. Specifically, if the standard
deviation of layer normalization is sufficiently large, the output of
Transformer stacks will converge to a specific low-rank subspace and result in
over-smoothing. To alleviate the over-smoothing problem, we consider
hierarchical fusion strategies, which combine the representations from
different layers adaptively to make the output more diverse. Extensive
experiment results on various data sets illustrate the effect of our fusion
method.
