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State Space Model for New-Generation Network Alternative to Transformers: A Survey (2404.09516v1)

Published 15 Apr 2024 in cs.LG, cs.AI, cs.CL, cs.CV, and cs.MM

Abstract: 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.

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Overview of State Space Model for New-Generation Networks: A Survey

The reviewed paper provides a thorough investigation into the potential of State Space Models (SSMs) as an efficient alternative to the prevalent Transformer architecture in neural networks. This survey is noteworthy for both its depth of analysis and breadth of scope, encompassing various application domains such as natural language processing, computer vision, and more.

Key Insights and Contributions

Central to the paper is the identification of the computational limitations inherent in the Transformer model, primarily due to its attention mechanism, which scales quadratically with input length. In response, the authors position SSMs as a robust alternative, capable of harnessing linear computational complexity while preserving a global receptive field.

  • Comprehensive Review: The survey synthesizes existing research on SSMs, outlining their mathematical foundation and diverse applications. It emphasizes the architecture’s ability to model long-range dependencies efficiently, making it suitable for various tasks.
  • Applications: The paper explores the versatile application of SSMs across multiple domains:
    • In natural language processing, SSMs emerge as a viable competitor to Transformers for LLMing tasks.
    • In computer vision, the surveyed models highlight improvements in tasks such as image segmentation and classification.
    • The exploration extends to graph data, multi-modal, and multi-media tasks, illustrating the model's adaptability.
  • Performance Comparisons: Through extensive experimentation on several downstream tasks like classification, tracking, and segmentation, the paper provides performance benchmarks. Although SSMs achieve competitive results, they yet trail compared to state-of-the-art Transformer networks.

Theoretical and Practical Implications

The paper’s exposition of SSMs suggests both theoretical and practical implications:

  1. Theoretical Framework: By framing the SSM as an evolution of recurrent neural networks and control theory principles, the authors offer a bridge for the integration of classical signal processing techniques into modern AI.
  2. Efficiency: Practical advantages of the SSM, such as reduced memory footprint and the capacity for handling longer sequences, present an opportunity for deploying AI in resource-constrained environments.
  3. Challenges and Opportunities: The survey identifies several challenges, notably the need for improved model performance against established benchmarks. The authors suggest research directions including scalable model architectures and novel scan operator designs to enhance SSM capabilities.

Future Prospects

The paper illuminates several promising paths for future research:

  • SSM-Transformer Hybrid Models: As research progresses, hybrid models combining the strengths of SSMs and Transformers could be explored to harness the computational efficiency of SSMs and the contextual richness of Transformers.
  • Domain-Specific Models: Tailoring SSMs for specific domains—such as remote sensing or real-time signal processing—could lead to breakthroughs in applying AI where Transformers, due to their computational demands, are currently impractical.
  • Pre-trained SSM Models: The development of large-scale pre-trained SSM models could catalyze adoption by providing versatile starting points for various AI applications, akin to current Transformer-based models.

In conclusion, while the State Space Model is in its nascent stage compared to the Transformer, its potential efficiency gains and adaptability signal a worthy avenue of research in the quest for more efficient AI architectures. This survey sets a comprehensive baseline, inviting the research community to further explore and innovate within this promising framework.

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Authors (16)
  1. Xiao Wang (507 papers)
  2. Shiao Wang (16 papers)
  3. Yuhe Ding (10 papers)
  4. Yuehang Li (7 papers)
  5. Wentao Wu (43 papers)
  6. Yao Rong (30 papers)
  7. Weizhe Kong (3 papers)
  8. Ju Huang (9 papers)
  9. Shihao Li (17 papers)
  10. Haoxiang Yang (13 papers)
  11. Ziwen Wang (37 papers)
  12. Bo Jiang (235 papers)
  13. Chenglong Li (94 papers)
  14. Yaowei Wang (149 papers)
  15. Yonghong Tian (184 papers)
  16. Jin Tang (139 papers)
Citations (39)
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