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| Kolmogorovove-Arnoldove siete× | Maskované autoenkodéry× | NeRF (Neural Radiance Fields)× | |
|---|---|---|---|
| Odbor | Hlboké učenie | Hlboké učenie | Hlboké učenie |
| Rodina | Machine learning | Machine learning | Machine learning |
| Rok vzniku≠ | 2024 | 2021 | 2020 |
| Tvorca≠ | Ziming Liu | Kaiming He | Ben Mildenhall |
| Typ | Neural network architecture | Neural network architecture | Neural network architecture |
| Pôvodný zdroj≠ | Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y., & Tegmark, M. (2024). KAN: Kolmogorov-Arnold Networks. arXiv preprint arXiv:2404.19756. 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 ↗ | Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision-ECCV 2020: 16th European Conference (pp. 405-421). Springer International Publishing. DOI ↗ |
| Ďalšie názvy | KAN, Kolmogorov-Arnold | MAE, Vision MAE | NeRF, Neural radiance field |
| Príbuzné | 4 | 4 | 4 |
| Zhrnutie≠ | Kolmogorov-Arnold Networks (KAN) is a neural network architecture introduced by Liu et al. in 2024 that replaces linear transformations with learned univariate functions on edges. Inspired by the Kolmogorov-Arnold representation theorem, KAN achieves superior function approximation with fewer parameters than traditional MLPs, offering potential efficiency gains and improved interpretability. | 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. | Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling novel view synthesis with photorealistic quality. |
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