방법 비교

	맘바 (상태 공간 모델)	Vision Mamba
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2023	2024
창시자	Albert Gu	Li Zhu
유형	Neural network architecture	Neural network architecture
원전	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.	Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning.
별칭	Mamba, State space models, Selective state space	ViM, Mamba for Vision
관련	4	4
요약	Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power.	Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity.