Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Mamba (Model de Spațiu de Stări)× | Autoencodere mascate× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2023 | 2021 |
| Autorul original≠ | Albert Gu | Kaiming He |
| Tip | Neural network architecture | Neural network architecture |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative≠ | Mamba, State space models, Selective state space | MAE, Vision MAE |
| Înrudite | 4 | 4 |
| Rezumat≠ | 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. |
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