Serially Disentangled Learning for Multi-Layered Neural Networks
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-08530-7_56
Reference21 articles.
1. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
2. Hu, J., et al.: Architecture disentanglement for deep neural networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 672–681 (2021)
3. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
4. Kamimura, R.: Neural self-compressor: collective interpretation by compressing multi-layered neural networks into non-layered networks. Neurocomputing 323, 12–36 (2019)
5. Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation strategies from data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 113–123 (2019)
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