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| Mamba (model prostora stanja)× | Masked Autoencoders× | Vision Mamba× | |
|---|---|---|---|
| Područje | Duboko učenje | Duboko učenje | Duboko učenje |
| Obitelj | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 2023 | 2021 | 2024 |
| Tvorac≠ | Albert Gu | Kaiming He | Li Zhu |
| Vrsta | Neural network architecture | Neural network architecture | Neural network architecture |
| Temeljni izvor≠ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ | He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16000-16009). DOI ↗ | 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. link ↗ |
| Drugi nazivi≠ | Mamba, State space models, Selective state space | MAE, Vision MAE | ViM, Mamba for Vision |
| Srodne | 4 | 4 | 4 |
| Sažetak≠ | 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. | Masked Autoencoders (MAE) is a self-supervised learning approach introduced by He et al. in 2021 that masks random patches of an image and trains a model to reconstruct the missing content. Adapting the masked language modeling paradigm from NLP to vision, MAE learns rich visual representations by solving a challenging reconstruction task without requiring labels. | 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. |
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