State Space Model for New-Generation Network Alternative to Transformers: A Survey
<|reference_start|>State Space Model for New-Generation Network Alternative to Transformers: A Survey: In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enormous computational demands of this architecture have deterred many researchers. To further reduce the complexity of attention models, numerous efforts have been made to design more efficient methods. Among them, the State Space Model (SSM), as a possible replacement for the self-attention based Transformer model, has drawn more and more attention in recent years. In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM. Specifically, we first give a detailed description of principles to help the readers quickly capture the key ideas of SSM. After that, we dive into the reviews of existing SSMs and their various applications, including natural language processing, computer vision, graph, multi-modal and multi-media, point cloud/event stream, time series data, and other domains. In addition, we give statistical comparisons and analysis of these models and hope it helps the readers to understand the effectiveness of different structures on various tasks. Then, we propose possible research points in this direction to better promote the development of the theoretical model and application of SSM. More related works will be continuously updated on the following GitHub: https://github.com/Event-AHU/Mamba_State_Space_Model_Paper_List.<|reference_end|>
@article{wang2024state,
    title={State Space Model for New-Generation Network Alternative to
  Transformers: A Survey},
    author={Xiao Wang, Shiao Wang, Yuhe Ding, Yuehang Li, Wentao Wu, Yao Rong,
  Weizhe Kong, Ju Huang, Shihao Li, Haoxiang Yang, Ziwen Wang, Bo Jiang,
  Chenglong Li, Yaowei Wang, Yonghong Tian, Jin Tang},
    journal={arXiv preprint arXiv:2404.09516},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09516},
    primaryClass={cs.LG cs.AI cs.CL cs.CV cs.MM}
}