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| Mamba (model prostora stanja)× | Masked Autoencoders× | Vision Transformer× | |
|---|---|---|---|
| Oblast | Duboko učenje | Duboko učenje | Duboko učenje |
| Porodica | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 2023 | 2021 | 2021 |
| Tvorac≠ | Albert Gu | Kaiming He | Dosovitskiy, A. et al. |
| Tip≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| 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 ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Drugi nazivi≠ | Mamba, State space models, Selective state space | MAE, Vision MAE | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Srodne≠ | 4 | 4 | 5 |
| 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. | The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs). |
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