# Depth-Adaptive Deep Neural Network Based on Learning Layer Relevance Weights
## 3 Depth-Adaptive Deep Neural Network
### Layer Pruning
* [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] Nonparametric Neural Networks. In Proceedings of the 5 th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24-26 April 2017; Available online: [[http://arxiv.org/abs/1712.05440](http://arxiv.org/abs/1712.05440)]([http://arxiv.org/abs/1712.05440](http://arxiv.org/abs/1712.05440)) (accessed on 15 February 2020). * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Neurosogenesis Deep Learning. In Proceedings of the International Joint Conference on Neural Networks, Perth, Australia, 27 November-1 December 2017; pp. 526-533. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Lifelong Learning with Dynamically Expandable Networks. In Proceedings of the 6 th International Conference on Learning Representations. (ICLR 2018), Vancouver, BC, Canada, 30 April-3 May 2018; Available online: [[http://arxiv.org/abs/1708.01547](http://arxiv.org/abs/1708.01547)]([http://arxiv.org/abs/1708.01547](http://arxiv.org/abs/1708.01547)) (accessed on 15 February 2020). * [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] [PERSON] Learning the number of neurons in deep networks. _Adv. Neural Inf. Process. Syst._**2016**, _29_, 2270-2278. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Pruning convolutional neural networks for resource efficient inference. In Proceedings of the 5 th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24-26 April 2017; pp. 1-17. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] Training very deep networks. _Adv. Neural Inf. Process. Syst._**2015**, _28_, 2377-2385. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep residual learning for image recognition. _Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit._**2016**, \(7\), 770-778. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep networks with stochastic depth. In _European Conference on Computer Vision_; Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2016; Volume 9908 LNCS, pp. 646-661. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Deeply-Fused Nets. _arXiv_**2016**, arXiv:1605.07716. * [PERSON] (2009) [PERSON] Learning deep architectures for AI. _Found. Trends Mach. Learn._**2009**, \(2\), 1-127. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification. _IEEE Trans. Geosci. Remote Sens._**2020**, _59_, 7831-7843. [CrossRef]
* [PERSON] (2002) [PERSON] Lagrange Multipliers. In _Six Sigma and Beyond_; CRC Press: Boca Raton, FL, USA, 2002; Volume 35, pp. 319-324. [CrossRef]
* [PERSON] (2021) [PERSON]; [PERSON] MNIST Handwritten Digit Database. AT&T Labs. Available online: [[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) (accessed on 3 June 2021). * [PERSON] (2009) [PERSON]; [PERSON] _Learning Multiple Layers of Features from Tiny Images_; Technical Report; University of Toronto: Toronto, ON, Canada, 2009; pp. 1-58. * [PERSON] (2015) [PERSON]; [PERSON] Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3 rd International Conference on Learning Representations, ICLR 2015--Conference Track Proceedings, San Diego, CA, USA, 7-9 May 2015. Available online: [[http://www.robots.ox.ac.uk/](http://www.robots.ox.ac.uk/)]([http://www.robots.ox.ac.uk/](http://www.robots.ox.ac.uk/)) (accessed on 21 September 2022). **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.