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| Latent Diffusion Models× | Mamba (State Space Model)× | Masked Autoencoders× | |
|---|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2022 | 2023 | 2021 |
| Urheber≠ | Robin Rombach | Albert Gu | Kaiming He |
| Typ | Neural network architecture | Neural network architecture | Neural network architecture |
| Wegweisende Quelle≠ | Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695). DOI ↗ | 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 ↗ |
| Aliasnamen≠ | LDM, Stable Diffusion, Latent Diffusion | Mamba, State space models, Selective state space | MAE, Vision MAE |
| Verwandt | 4 | 4 | 4 |
| Zusammenfassung≠ | Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality. | 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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